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Helion Needs You

from Sam Altman [alt+shift+b] in AI

Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator. The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period. If both things happen, it will transform the world. Abundant, clean, and radically inexpensive energy will elevate the quality of life for all of us—think about how much the cost of energy factors into what we do and use. Also, electricity at this price will allow us to do things like efficiently capture carbon (so although we’ll still rely on gasoline for awhile, it’ll be ok). Although Helion’s scientific progress of the past 8 years is phenomenal and necessary, it is not sufficient to rapidly get to this new energy economy. Helion now needs to figure out how to engineer machines that don’t break, how to build a factory and supply chain capable of manufacturing a machine every day, how to work with power grids and governments around the world, and more. The biggest input to the degree and speed of success at the company is now the talent of the people who join the team. Here are a few of the most critical jobs, but please don’t let the lack of a perfect fit deter you from applying. Electrical Engineer, Low Voltage: https://boards.greenhouse.io/helionenergy/jobs/4044506005 Electrical Engineer, Pulsed Power: https://boards.greenhouse.io/helionenergy/jobs/4044510005 Mechanical Engineer, Generator Systems: https://boards.greenhouse.io/helionenergy/jobs/4044522005 Manager of Mechanical Engineering: https://boards.greenhouse.io/helionenergy/jobs/4044521005 (All current jobs: https://www.helionenergy.com/careers/)
13th Jul 2022

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More from Sam Altman

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Here is a photo of my family. I love them more than anything. Images have power, I hope. Normally we try to be pretty private, but in this case I am sharing a photo in the hopes that it might dissuade the next person from throwing a Molotov cocktail at our house, no matter what they think about me. The first person did it last night, at 3:45 am in the morning. Thankfully it bounced off the house and no one got hurt. Words have power too. There was an incendiary article about me a few days ago. Someone said to me yesterday they thought it was coming at a time of great anxiety about AI and that it made things more dangerous for me. I brushed it aside. Now I am awake in the middle of the night and pissed, and thinking that I have underestimated the power of words and narratives. This seems like as good of a time as any to address a few things.  First, what I believe. *Working towards prosperity for everyone, empowering all people, and advancing science and technology are moral obligations for me. *AI will be the most powerful tool for expanding human capability and potential that anyone has ever seen. Demand for this tool will be essentially uncapped, and people will do incredible things with it. The world deserves huge amounts of AI and we must figure out how to make it happen. *It will not all go well. The fear and anxiety about AI is justified; we are in the process of witnessing the largest change to society in a long time, and perhaps ever. We have to get safety right, which is not just about aligning a model—we urgently need a society-wide response to be resilient to new threats. This includes things like new policy to help navigate through a difficult economic transition in order to get to a much better future.  *AI has to be democratized; power cannot be too concentrated. Control of the future belongs to all people and their institutions. AI needs to empower people individually, and we need to make decisions about our future and the new rules collectively. I do not think it is right that a few AI labs would make the most consequential decisions about the shape of our future. *Adaptability is critical. We are all learning about something new very quickly; some of our beliefs will be right and some will be wrong, and sometimes we will need to change our mind quickly as the technology develops and society evolves. No one understands the impacts of superintelligence yet, but they will be immense.   Second, some personal reflections. As I reflect on my own work in the first decade of OpenAI, I can point to a lot of things I’m proud of and a bunch of mistakes. I was thinking about our upcoming trial with Elon and remembering how much I held the line on not being willing to agree to the unilateral control he wanted over OpenAI. I’m proud of that, and the narrow path we navigated then to allow the continued existence of OpenAI, and all the achievements that followed. I am not proud of being conflict-averse, which has caused great pain for me and OpenAI. I am not proud of handling myself badly in a conflict with our previous board that led to a huge mess for the company. I have made many other mistakes throughout the insane trajectory of OpenAI; I am a flawed person in the center of an exceptionally complex situation, trying to get a little better each year, always working for the mission. We knew going into this how huge the stakes of AI were, and that the personal disagreements between well-meaning people I cared about would be amplified greatly. But it’s another thing to live through these bitter conflicts and often to have to arbitrate them, and the costs have been serious. I am sorry to people I’ve hurt and wish I had learned more faster. I am also very aware that OpenAI is now a major platform, not a scrappy startup, and we need to operate in a more predictable way now. It has been an extremely intense, chaotic, and high-pressure few years.  Mostly though, I am extremely proud that we are delivering on our mission, which seemed incredibly unlikely when we started. Against all odds, we figured out how to build very powerful AI, figured out how to amass enough capital to build the infrastructure to deliver it, figured out how to build a product company and business, figured out how to deliver reasonably safe and robust services at a massive scale, and much more. A lot of companies say they are going to change the world; we actually did. Third, some thoughts about the industry. My personal takeaway from the last several years, and take on why there has been so much Shakespearean drama between the companies in our field, comes down to this: “Once you see AGI you can’t unsee it.” It has a real "ring of power” dynamic to it, and makes people do crazy things. I don’t mean that AGI is the ring itself, but instead the totalizing philosophy of “being the one to control AGI”. The only solution I can come up with is to orient towards sharing the technology with people broadly, and for no one to have the ring. The two obvious ways to do this are individual empowerment and making sure democratic system stays in control. It is important that the democratic process remains more powerful than companies. Laws and norms are going to change, but we have to work within the democratic process, even though it will be messy and slower than we’d like. We want to be a voice and a stakeholder, but not to have all the power. A lot of the criticism of our industry comes from sincere concern about the incredibly high stakes of this technology. This is quite valid, and we welcome good-faith criticism and debate. I empathize with anti-technology sentiments and clearly technology isn’t always good for everyone. But overall, I believe technological progress can make the future unbelievably good, for your family and mine.  While we have that debate, we should de-escalate the rhetoric and tactics and try to have fewer explosions in fewer homes, figuratively and literally.

10th Apr 2026 • 1 votes
Jakub and Szymon

AI has gotten remarkably better in recent years; ChatGPT can do amazing things that we take for granted. This is as it should be, and is the story of human progress. But behind the blinking circle, nicely abstracted away, is the greatest story of human ingenuity I have ever seen. A lot of people have worked unbelievably hard to discover how to build something that most experts thought was impossible on this timeframe, and to build a company to deliver products at massive scale to let people benefit from it. Most people who use ChatGPT will never think about the people that put so much work into it, which is totally ok, but just to take a minute of your time… There are two people I'd like to mention that OpenAI would not be OpenAI without: Jakub Pachocki and Szymon Sidor. Time and again, they combine research and engineering to solve impossible problems. They have not gotten enough public credit, but they decided to scale up RL as a baseline to see where it broke when the conventional wisdom was that it didn't scale which led to our Dota result, built much of the infrastructure that enabled a lot of our scientific discoveries, led GPT-4 pretraining, drove together with Ilya and Lukasz the initial ideas that led to the reasoning breakthrough, and have made significant progress exploring new paradigms. Jakub is our chief scientist. He once described Szymon as “indefatigable”, which is as perfect of a use of that word as I have ever heard. OpenAI has not yet thrown a problem at them they have not been able to solve; I have heard about partnerships like there is research labs of the past where two people are able to complement each other so well, but it is very special to get to watch it unfold over the years.

9th Sep 2025 • 1 votes
The Gentle Singularity

We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be. Robots are not yet walking the streets, nor are most of us talking to AI all day. People still die of disease, we still can’t easily go to space, and there is a lot about the universe we don’t understand. And yet, we have recently built systems that are smarter than people in many ways, and are able to significantly amplify the output of people using them. The least-likely part of the work is behind us; the scientific insights that got us to systems like GPT-4 and o3 were hard-won, but will take us very far. AI will contribute to the world in many ways, but the gains to quality of life from AI driving faster scientific progress and increased productivity will be enormous; the future can be vastly better than the present. Scientific progress is the biggest driver of overall progress; it’s hugely exciting to think about how much more we could have. In some big sense, ChatGPT is already more powerful than any human who has ever lived. Hundreds of millions of people rely on it every day and for increasingly important tasks; a small new capability can create a hugely positive impact; a small misalignment multiplied by hundreds of millions of people can cause a great deal of negative impact. 2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same. 2026 will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world. A lot more people will be able to create software, and art. But the world wants a lot more of both, and experts will probably still be much better than novices, as long as they embrace the new tools. Generally speaking, the ability for one person to get much more done in 2030 than they could in 2020 will be a striking change, and one many people will figure out how to benefit from. In the most important ways, the 2030s may not be wildly different. People will still love their families, express their creativity, play games, and swim in lakes. But in still-very-important-ways, the 2030s are likely going to be wildly different from any time that has come before. We do not know how far beyond human-level intelligence we can go, but we are about to find out. In the 2030s, intelligence and energy—ideas, and the ability to make ideas happen—are going to become wildly abundant. These two have been the fundamental limiters on human progress for a long time; with abundant intelligence and energy (and good governance), we can theoretically have anything else. Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes. We already hear from scientists that they are two or three times more productive than they were before AI. Advanced AI is interesting for many reasons, but perhaps nothing is quite as significant as the fact that we can use it to do faster AI research. We may be able to discover new computing substrates, better algorithms, and who knows what else. If we can do a decade’s worth of research in a year, or a month, then the rate of progress will obviously be quite different. From here on, the tools we have already built will help us find further scientific insights and aid us in creating better AI systems. Of course this isn’t the same thing as an AI system completely autonomously updating its own code, but nevertheless this is a larval version of recursive self-improvement. There are other self-reinforcing loops at play. The economic value creation has started a flywheel of compounding infrastructure buildout to run these increasingly-powerful AI systems. And robots that can build other robots (and in some sense, datacenters that can build other datacenters) aren’t that far off.  If we have to make the first million humanoid robots the old-fashioned way, but then they can operate the entire supply chain—digging and refining minerals, driving trucks, running factories, etc.—to build more robots, which can build more chip fabrication facilities, data centers, etc, then the rate of progress will obviously be quite different. As datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity. (People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.) The rate of technological progress will keep accelerating, and it will continue to be the case that people are capable of adapting to almost anything. There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before. We probably won’t adopt a new social contract all at once, but when we look back in a few decades, the gradual changes will have amounted to something big. If history is any guide, we will figure out new things to do and new things to want, and assimilate new tools quickly (job change after the industrial revolution is a good recent example). Expectations will go up, but capabilities will go up equally quickly, and we’ll all get better stuff. We will build ever-more-wonderful things for each other. People have a long-term important and curious advantage over AI: we are hard-wired to care about other people and what they think and do, and we don’t care very much about machines. A subsistence farmer from a thousand years ago would look at what many of us do and say we have fake jobs, and think that we are just playing games to entertain ourselves since we have plenty of food and unimaginable luxuries. I hope we will look at the jobs a thousand years in the future and think they are very fake jobs, and I have no doubt they will feel incredibly important and satisfying to the people doing them. The rate of new wonders being achieved will be immense. It’s hard to even imagine today what we will have discovered by 2035; maybe we will go from solving high-energy physics one year to beginning space colonization the next year; or from a major materials science breakthrough one year to true high-bandwidth brain-computer interfaces the next year. Many people will choose to live their lives in much the same way, but at least some people will probably decide to “plug in”. Looking forward, this sounds hard to wrap our heads around. But probably living through it will feel impressive but manageable. From a relativistic perspective, the singularity happens bit by bit, and the merge happens slowly. We are climbing the long arc of exponential technological progress; it always looks vertical looking forward and flat going backwards, but it’s one smooth curve. (Think back to 2020, and what it would have sounded like to have something close to AGI by 2025, versus what the last 5 years have actually been like.) There are serious challenges to confront along with the huge upsides. We do need to solve the safety issues, technically and societally, but then it’s critically important to widely distribute access to superintelligence given the economic implications. The best path forward might be something like: Solve the alignment problem, meaning that we can robustly guarantee that we get AI systems to learn and act towards what we collectively really want over the long-term (social media feeds are an example of misaligned AI; the algorithms that power those are incredible at getting you to keep scrolling and clearly understand your short-term preferences, but they do so by exploiting something in your brain that overrides your long-term preference). Then focus on making superintelligence cheap, widely available, and not too concentrated with any person, company, or country. Society is resilient, creative, and adapts quickly. If we can harness the collective will and wisdom of people, then although we’ll make plenty of mistakes and some things will go really wrong, we will learn and adapt quickly and be able to use this technology to get maximum upside and minimal downside. Giving users a lot of freedom, within broad bounds society has to decide on, seems very important. The sooner the world can start a conversation about what these broad bounds are and how we define collective alignment, the better. We (the whole industry, not just OpenAI) are building a brain for the world. It will be extremely personalized and easy for everyone to use; we will be limited by good ideas. For a long time, technical people in the startup industry have made fun of “the idea guys”; people who had an idea and were looking for a team to build it. It now looks to me like they are about to have their day in the sun. OpenAI is a lot of things now, but before anything else, we are a superintelligence research company. We have a lot of work in front of us, but most of the path in front of us is now lit, and the dark areas are receding fast. We feel extraordinarily grateful to get to do what we do. Intelligence too cheap to meter is well within grasp. This may sound crazy to say, but if we told you back in 2020 we were going to be where we are today, it probably sounded more crazy than our current predictions about 2030. May we scale smoothly, exponentially and uneventfully through superintelligence.

10th Jun 2025 • 1 votes
Three Observations

Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity.  Systems that start to point to AGI* are coming into view, and so we think it’s important to understand the moment we are in. AGI is a weakly defined term, but generally speaking we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields. People are tool-builders with an inherent drive to understand and create, which leads to the world getting better for all of us. Each new generation builds upon the discoveries of the generations before to create even more capable tools—electricity, the transistor, the computer, the internet, and soon AGI. Over time, in fits and starts, the steady march of human innovation has brought previously unimaginable levels of prosperity and improvements to almost every aspect of people’s lives. In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together. In another sense, it is the beginning of something for which it’s hard not to say “this time it’s different”; the economic growth in front of us looks astonishing, and we can now imagine a world where we cure all diseases, have much more time to enjoy with our families, and can fully realize our creative potential. In a decade, perhaps everyone on earth will be capable of accomplishing more than the most impactful person can today. We continue to see rapid progress with AI development. Here are three observations about the economics of AI: 1. The intelligence of an AI model roughly equals the log of the resources used to train and run it. These resources are chiefly training compute, data, and inference compute. It appears that you can spend arbitrary amounts of money and get continuous and predictable gains; the scaling laws that predict this are accurate over many orders of magnitude. 2. The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time period. Moore’s law changed the world at 2x every 18 months; this is unbelievably stronger.  3. The socioeconomic value of linearly increasing intelligence is super-exponential in nature. A consequence of this is that we see no reason for exponentially increasing investment to stop in the near future. If these three observations continue to hold true, the impacts on society will be significant. We are now starting to roll out AI agents, which will eventually feel like virtual co-workers. Let’s imagine the case of a software engineering agent, which is an agent that we expect to be particularly important. Imagine that this agent will eventually be capable of doing most things a software engineer at a top company with a few years of experience could do, for tasks up to a couple of days long. It will not have the biggest new ideas, it will require lots of human supervision and direction, and it will be great at some things but surprisingly bad at others. Still, imagine it as a real-but-relatively-junior virtual coworker. Now imagine 1,000 of them. Or 1 million of them. Now imagine such agents in every field of knowledge work. In some ways, AI may turn out to be like the transistor economically—a big scientific discovery that scales well and that seeps into almost every corner of the economy. We don’t think much about transistors, or transistor companies, and the gains are very widely distributed. But we do expect our computers, TVs, cars, toys, and more to perform miracles. The world will not change all at once; it never does. Life will go on mostly the same in the short run, and people in 2025 will mostly spend their time in the same way they did in 2024. We will still fall in love, create families, get in fights online, hike in nature, etc. But the future will be coming at us in a way that is impossible to ignore, and the long-term changes to our society and economy will be huge. We will find new things to do, new ways to be useful to each other, and new ways to compete, but they may not look very much like the jobs of today.  Agency, willfulness, and determination will likely be extremely valuable. Correctly deciding what to do and figuring out how to navigate an ever-changing world will have huge value; resilience and adaptability will be helpful skills to cultivate. AGI will be the biggest lever ever on human willfulness, and enable individual people to have more impact than ever before, not less. We expect the impact of AGI to be uneven. Although some industries will change very little, scientific progress will likely be much faster than it is today; this impact of AGI may surpass everything else. The price of many goods will eventually fall dramatically (right now, the cost of intelligence and the cost of energy constrain a lot of things), and the price of luxury goods and a few inherently limited resources like land may rise even more dramatically. Technically speaking, the road in front of us looks fairly clear. But public policy and collective opinion on how we should integrate AGI into society matter a lot; one of our reasons for launching products early and often is to give society and the technology time to co-evolve. AI will seep into all areas of the economy and society; we will expect everything to be smart. Many of us expect to need to give people more control over the technology than we have historically, including open-sourcing more, and accept that there is a balance between safety and individual empowerment that will require trade-offs. While we never want to be reckless and there will likely be some major decisions and limitations related to AGI safety that will be unpopular, directionally, as we get closer to achieving AGI, we believe that trending more towards individual empowerment is important; the other likely path we can see is AI being used by authoritarian governments to control their population through mass surveillance and loss of autonomy. Ensuring that the benefits of AGI are broadly distributed is critical. The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas. In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention. We are open to strange-sounding ideas like giving some “compute budget” to enable everyone on Earth to use a lot of AI, but we can also see a lot of ways where just relentlessly driving the cost of intelligence as low as possible has the desired effect. Anyone in 2035 should be able to marshall the intellectual capacity equivalent to everyone in 2025; everyone should have access to unlimited genius to direct however they can imagine. There is a great deal of talent right now without the resources to fully express itself, and if we change that, the resulting creative output of the world will lead to tremendous benefits for us all. Thanks especially to Josh Achiam, Boaz Barak and Aleksander Madry for reviewing drafts of this. *By using the term AGI here, we aim to communicate clearly, and we do not intend to alter or interpret the definitions and processes that define our relationship with Microsoft. We fully expect to be partnered with Microsoft for the long term. This footnote seems silly, but on the other hand we know some journalists will try to get clicks by writing something silly so here we are pre-empting the silliness…

9th Feb 2025 • 62 votes
Reflections

The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning. New years get people in a reflective mood, and I wanted to share some personal thoughts about how it has gone so far, and some of the things I’ve learned along the way. As we get closer to AGI, it feels like an important time to look at the progress of our company. There is still so much to understand, still so much we don’t know, and it’s still so early. But we know a lot more than we did when we started. We started OpenAI almost nine years ago because we believed that AGI was possible, and that it could be the most impactful technology in human history. We wanted to figure out how to build it and make it broadly beneficial; we were excited to try to make our mark on history. Our ambitions were extraordinarily high and so was our belief that the work might benefit society in an equally extraordinary way. At the time, very few people cared, and if they did, it was mostly because they thought we had no chance of success. In 2022, OpenAI was a quiet research lab working on something temporarily called “Chat With GPT-3.5”. (We are much better at research than we are at naming things.) We had been watching people use the playground feature of our API and knew that developers were really enjoying talking to the model. We thought building a demo around that experience would show people something important about the future and help us make our models better and safer. We ended up mercifully calling it ChatGPT instead, and launched it on November 30th of 2022. We always knew, abstractly, that at some point we would hit a tipping point and the AI revolution would get kicked off. But we didn’t know what the moment would be. To our surprise, it turned out to be this. The launch of ChatGPT kicked off a growth curve like nothing we have ever seen—in our company, our industry, and the world broadly. We are finally seeing some of the massive upside we have always hoped for from AI, and we can see how much more will come soon. It hasn’t been easy. The road hasn’t been smooth and the right choices haven’t been obvious. In the last two years, we had to build an entire company, almost from scratch, around this new technology. There is no way to train people for this except by doing it, and when the technology category is completely new, there is no one at all who can tell you exactly how it should be done. Building up a company at such high velocity with so little training is a messy process. It’s often two steps forward, one step back (and sometimes, one step forward and two steps back). Mistakes get corrected as you go along, but there aren’t really any handbooks or guideposts when you’re doing original work. Moving at speed in uncharted waters is an incredible experience, but it is also immensely stressful for all the players. Conflicts and misunderstanding abound. These years have been the most rewarding, fun, best, interesting, exhausting, stressful, and—particularly for the last two—unpleasant years of my life so far. The overwhelming feeling is gratitude; I know that someday I’ll be retired at our ranch watching the plants grow, a little bored, and will think back at how cool it was that I got to do the work I dreamed of since I was a little kid. I try to remember that on any given Friday, when seven things go badly wrong by 1 pm. A little over a year ago, on one particular Friday, the main thing that had gone wrong that day was that I got fired by surprise on a video call, and then right after we hung up the board published a blog post about it. I was in a hotel room in Las Vegas. It felt, to a degree that is almost impossible to explain, like a dream gone wrong. Getting fired in public with no warning kicked off a really crazy few hours, and a pretty crazy few days. The “fog of war” was the strangest part. None of us were able to get satisfactory answers about what had happened, or why.  The whole event was, in my opinion, a big failure of governance by well-meaning people, myself included. Looking back, I certainly wish I had done things differently, and I’d like to believe I’m a better, more thoughtful leader today than I was a year ago. I also learned the importance of a board with diverse viewpoints and broad experience in managing a complex set of challenges. Good governance requires a lot of trust and credibility. I appreciate the way so many people worked together to build a stronger system of governance for OpenAI that enables us to pursue our mission of ensuring that AGI benefits all of humanity. My biggest takeaway is how much I have to be thankful for and how many people I owe gratitude towards: to everyone who works at OpenAI and has chosen to spend their time and effort going after this dream, to friends who helped us get through the crisis moments, to our partners and customers who supported us and entrusted us to enable their success, and to the people in my life who showed me how much they cared. [1] We all got back to the work in a more cohesive and positive way and I’m very proud of our focus since then. We have done what is easily some of our best research ever. We grew from about 100 million weekly active users to more than 300 million. Most of all, we have continued to put technology out into the world that people genuinely seem to love and that solves real problems. Nine years ago, we really had no idea what we were eventually going to become; even now, we only sort of know. AI development has taken many twists and turns and we expect more in the future. Some of the twists have been joyful; some have been hard. It’s been fun watching a steady stream of research miracles occur, and a lot of naysayers have become true believers. We’ve also seen some colleagues split off and become competitors. Teams tend to turn over as they scale, and OpenAI scales really fast. I think some of this is unavoidable—startups usually see a lot of turnover at each new major level of scale, and at OpenAI numbers go up by orders of magnitude every few months. The last two years have been like a decade at a normal company. When any company grows and evolves so fast, interests naturally diverge. And when any company in an important industry is in the lead, lots of people attack it for all sorts of reasons, especially when they are trying to compete with it. Our vision won’t change; our tactics will continue to evolve. For example, when we started we had no idea we would have to build a product company; we thought we were just going to do great research. We also had no idea we would need such a crazy amount of capital. There are new things we have to go build now that we didn’t understand a few years ago, and there will be new things in the future we can barely imagine now.  We are proud of our track-record on research and deployment so far, and are committed to continuing to advance our thinking on safety and benefits sharing. We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology safer. We believe in the importance of being world leaders on safety and alignment research, and in guiding that research with feedback from real world applications. We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes. We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word. We love our current products, but we are here for the glorious future. With superintelligence, we can do anything else. Superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own, and in turn massively increase abundance and prosperity. This sounds like science fiction right now, and somewhat crazy to even talk about it. That’s alright—we’ve been there before and we’re OK with being there again. We’re pretty confident that in the next few years, everyone will see what we see, and that the need to act with great care, while still maximizing broad benefit and empowerment, is so important. Given the possibilities of our work, OpenAI cannot be a normal company. How lucky and humbling it is to be able to play a role in this work. (Thanks to Josh Tyrangiel for sort of prompting this. I wish we had had a lot more time.) [1] There were a lot of people who did incredible and gigantic amounts of work to help OpenAI, and me personally, during those few days, but two people stood out from all others. Ron Conway and Brian Chesky went so far above and beyond the call of duty that I’m not even sure how to describe it. I’ve of course heard stories about Ron’s ability and tenaciousness for years and I’ve spent a lot of time with Brian over the past couple of years getting a huge amount of help and advice. But there’s nothing quite like being in the foxhole with people to see what they can really do. I am reasonably confident OpenAI would have fallen apart without their help; they worked around the clock for days until things were done. Although they worked unbelievably hard, they stayed calm and had clear strategic thought and great advice throughout. They stopped me from making several mistakes and made none themselves. They used their vast networks for everything needed and were able to navigate many complex situations. And I’m sure they did a lot of things I don’t know about. What I will remember most, though, is their care, compassion, and support. I thought I knew what it looked like to support a founder and a company, and in some small sense I did. But I have never before seen, or even heard of, anything like what these guys did, and now I get more fully why they have the legendary status they do. They are different and both fully deserve their genuinely unique reputations, but they are similar in their remarkable ability to move mountains and help, and in their unwavering commitment in times of need. The tech industry is far better off for having both of them in it. There are others like them; it is an amazingly special thing about our industry and does much more to make it all work than people realize. I look forward to paying it forward. On a more personal note, thanks especially to Ollie for his support that weekend and always; he is incredible in every way and no one could ask for a better partner.

6th Jan 2025 • 118 votes

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BAIR at CoRL 2026

The Conference on Robot Learning (CoRL) takes place November 9–12, 2026, in Austin, Texas. BAIR faculty, postdocs and students will be presenting research at various sessions, including an Early Career Keynote from Professor Negar Mehr. See below for the full schedule of sessions with BAIR members, including papers co-authored with collaborators at other universities and companies. h2 { border-bottom: 2px solid #003262; padding-bottom: 0.3rem; margin-bottom: 0.2rem; } .corl-day-sub { font-size: 0.8rem; color: #666; margin: 0 0 1em; } .corl-day-sub span + span::before { content: "·"; margin: 0 0.5em; color: #aaa; } .corl-session { display: grid; grid-template-columns: 7.5rem 1fr; gap: 1rem; padding: 1rem 0; border-top: 1px solid #eee; } .corl-session:first-of-type { border-top: 0; } .corl-session.corl-featured { background: #fffbf0; border-left: 3px solid #FDB515; padding-left: 0.8rem; } .corl-time { font-weight: 700; font-size: 0.9rem; line-height: 1.3; } .corl-time small { display: block; font-weight: 400; font-size: 0.7rem; color: #777; text-transform: uppercase; letter-spacing: 0.03em; } .corl-tag { display: inline-block; font-size: 0.7rem; font-weight: 700; text-transform: uppercase; letter-spacing: 0.04em; padding: 0.1rem 0.45rem; border-radius: 3px; background: #eef2f7; color: #003262; } .corl-tag.k-keynote { background: #FDB515; color: #1a1a1a; } .corl-tag.k-oral { background: #003262; color: #fff; } .corl-tag.k-spotlight { background: #3b7ea1; color: #fff; } .corl-tag.k-poster { background: #eef2f7; color: #003262; } .corl-tag.k-lbr { background: #e7f3ec; color: #145a32; } .corl-tag.k-workshop { background: #f1ecf7; color: #4a2a75; } .corl-session h3 { margin: 0.35rem 0 0.15rem; font-size: 1.05rem; } .corl-venue { font-size: 0.8rem; color: #666; margin: 0 0 0.6em; } .corl-chair { font-weight: 700; color: #333; } .corl-paper, .corl-appearance { margin: 0 0 0.9em; } .corl-paper h4, .corl-appearance h4 { font-size: 0.92rem; margin: 0 0 0.2em; line-height: 1.35; font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; font-weight: 700; } .corl-board { display: inline-block; font-size: 0.72rem; color: #555; border: 1px solid #ddd; border-radius: 3px; padding: 0 0.35rem; margin-bottom: 0.25em; } .corl-authors { font-size: 0.82rem; margin: 0 0 0.2em; line-height: 1.45; } .corl-also, .corl-sources, .corl-program, .corl-see { font-size: 0.75rem; color: #666; margin: 0 0 0.15em; } .corl-see { font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; font-style: italic; } .corl-talk { font-style: italic; margin: 0 0 0.2em; } .corl-appearance p { font-size: 0.85rem; margin: 0 0 0.2em; } .corl-organizers { margin: 0; padding-left: 1.1rem; } .corl-organizers li { margin-bottom: 0.5em; font-size: 0.88rem; } .corl-organizers p { margin: 0; font-size: 0.82rem; color: #444; } .corl-about { margin-top: 2.5em; border-top: 2px solid #003262; padding-top: 0.5em; font-size: 0.85rem; } .corl-about-links { font-family: 'Lato', 'Helvetica Neue', Helvetica, sans-serif; display: flex; flex-wrap: wrap; gap: 0.4rem 1.2rem; } @media (max-width: 40em) { .corl-stats { grid-template-columns: repeat(2, 1fr); } .corl-session { grid-template-columns: 1fr; gap: 0.3rem; } .corl-time small { display: inline; margin-left: 0.4em; } .corl-time { font-size: 1rem; } .corl-venue, .corl-also, .corl-sources, .corl-program, .corl-board, .corl-tag, .corl-time small, .corl-day-sub, .corl-stat small, .corl-nav-note, .corl-see { font-size: 0.8rem; } .corl-authors { font-size: 0.9rem; } } @media print { .corl-tools, .corl-status, .corl-nav, .corl-empty { display: none !important; } } November 9–12, 2026Austin, TexasAll times CST · UTC−6 33Main-conference papers 4Late-breaking results (LBR) posters 10Orals & spotlights3 orals · 7 spotlights 4Days of researchNovember 9–12 MonNov 9 TueNov 10 WedNov 11 ThuNov 12 Main conference Nov 9–11 · Workshops Nov 12 Times shown for oral and spotlight papers are session windows. Individual presentation order is not specified. Papers appear in both their talk and poster sessions. Paper inclusion: At least one author is affiliated with UC Berkeley; joint affiliations qualify. Faculty talks, session-chair roles and workshop participation are also included. Late-breaking results: Four late-breaking posters with Berkeley authors are listed so far. This list may grow as more late-breaking posters are confirmed. Find a paper or person Session typeAll sessionsKeynoteOralsSpotlightsPostersLate-breaking resultsWorkshops Reset Showing all sessionsUpdated November 8, 2026 No matching sessions. Try a surname, a shorter paper title, or reset the filters. Monday, November 9 Main conference10 papers + 3 LBR posters 9:00–9:30 AMCST Spotlight · S1 Generalist policies, memory and transfer Hilton Austin MEM: Multi-Scale Embodied Memory for Vision Language Action Models Marcel Torne, Karl Pertsch, Homer Walke, Kyle Vedder, Suraj Nair, Brian Ichter, Allen Z. Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Robert Equi, Quan Vuong, Jost Tobias Springenberg, Sergey Levine, Chelsea Finn, Danny Driess Also at Poster Session I · 5:00–6:30 PM · Poster 42 · JW Marriott Paper ↗ Diagnosing Compositional Generalization in Sequential Robot Tasks Yixiao Wang, Cheng-En Wu, Lingfeng Sun, Pengcheng Wang, Xiang Ji, Boyuan Liang, Guojian Zhan, Masayoshi Tomizuka Also at Poster Session I · 5:00–6:30 PM · Poster 115 · JW Marriott Paper ↗ 12:00–1:30 PMCST · LUNCH BREAK Late-breaking results · Posters Agentic Robotics Griffin · Level 2 · JW Marriott Austin Griffin board 119 · LBR #15 Scaling Online Exploration for Robot Agents Runhan Huang, Xiangcheng Zhang, Haoyu Chen, Yitong Li, Yen-Jen Wang, Bohan Lyu, Haotian Ye, Chuye Hong, Yixuan Wang, Chloe Su, Haoyu Xiong, Yangzhen Wu, Enze Xie, Travis Manderson, Stefano Ermon, Pieter Abbeel, Antonio Torralba, Dawn Song, Weirui Ye, Yilun Du Griffin board 120 · LBR #19 Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi Also at Agentic Robotics workshop · Thursday, November 12 · 4:15–5:55 PM Griffin board 131 · LBR #54 Flip Anything with Agentic Robotics Self-Learning Shuangyu Xie, Kaiyuan Chen, Ken Goldberg GitHub ↗ Late-breaking results program ↗ 2:00–2:30 PMCST Keynote Early Career Keynote II Hilton Austin Session chair: Ken Goldberg Professor Negar Mehr How Can Robots Learn to Interact and Coordinate with Others? Session details ↗ 5:00–6:30 PMCST Posters Poster Session I JW Marriott Poster 42 MEM: Multi-Scale Embodied Memory for Vision Language Action Models Marcel Torne, Karl Pertsch, Homer Walke, Kyle Vedder, Suraj Nair, Brian Ichter, Allen Z. Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Robert Equi, Quan Vuong, Jost Tobias Springenberg, Sergey Levine, Chelsea Finn, Danny Driess Authors listed under Monday, Spotlight S1 Paper ↗ Poster 57 Large Video Planner Enables Generalizable Robot Control Boyuan Chen, Tianyuan Zhang, Haoran Geng, Caiyi Zhang, Peihao Li, Kiwhan Song, William T. Freeman, Jitendra Malik, Pieter Abbeel, Russ Tedrake, Vincent Sitzmann, Yilun Du Paper ↗ Poster 68 How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik Paper ↗ Poster 97 HiRE: Hindsight Reward Editing for Policy Finetuning Haoyi Niu, Zhengtao Han, Yufeng Ji, Zhongyu Li, Koushil Sreenath Paper ↗ Poster 113 Scalable Behavior Cloning with Open Data, Training, and Evaluation Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh, Adam Rashid, Hongsuk Choi, David McAllister, Justin Yu, Yiyuan Chen, Huang Huang, Pieter Abbeel, Xi Chen, Rocky Duan, Phillip Isola, Jitendra Malik, Fred Shentu, Guanya Shi, Philipp Wu, Angjoo Kanazawa Paper ↗ Poster 115 Diagnosing Compositional Generalization in Sequential Robot Tasks Yixiao Wang, Cheng-En Wu, Lingfeng Sun, Pengcheng Wang, Xiang Ji, Boyuan Liang, Guojian Zhan, Masayoshi Tomizuka Authors listed under Monday, Spotlight S1 Paper ↗ Poster 142 AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge Noriaki Hirose, Catherine Glossop, Dhruv Shah, Sergey Levine Paper ↗ Poster 159 GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation Yousef Liang, Ryan Ardalan, Yarden Goraly, Chenhao Zhu, Jonas Frey, Claire Tomlin, Marco Pavone, S. Shankar Sastry Paper ↗ Poster 202 ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Wenli Xiao, Jia Xie, Tonghe Zhang, Haotian Lin, Letian Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi Fan, Yuke Zhu, Guanya Shi Paper ↗ Poster 225 Energy-Based Policy Orchestration Weirui Ye, Yen-Jen Wang, Elvis Hsieh, Zhiyang Dou, Shibo Zhao, Pieter Abbeel, Yilun Du, Leslie Pack Kaelbling, Tomás Lozano-Pérez Paper ↗ Tuesday, November 10 Main conference11 papers + 1 LBR poster 9:00–9:30 AMCST Oral · O4 Reinforcement learning and motion reasoning Hilton Austin Flow Policy Gradients for Robot Control Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa Also at Poster Session II · 5:00–6:30 PM · Poster 63 · JW Marriott Paper ↗ 10:30–11:00 AMCST Oral · O5 Dexterous learning, grasping and assembly Hilton Austin EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data Ruijie Zheng, Dantong Niu, Yuqi Xie, Jing Wang, Mengda Xu, Yunfan Jiang, Fernando Castañeda, Fengyuan Hu, You Liang Tan, Letian Fu, Trevor Darrell, Furong Huang, Yuke Zhu, Danfei Xu, Linxi Fan Also at Poster Session II · 5:00–6:30 PM · Poster 54 · JW Marriott Paper ↗ 11:30 AM–12:00 PMCST Oral · O7 Humanoid data, control and skills Hilton Austin Session chair: Koushil Sreenath SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz, Pieter Abbeel, Koushil Sreenath Also at Poster Session II · 5:00–6:30 PM · Poster 217 · JW Marriott Paper ↗ 12:00–1:30 PMCST · LUNCH BREAK Late-breaking results · Posters Real2Sim2Real Griffin · Level 2 · JW Marriott Austin Griffin board 122 · LBR #26 RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks Kaiyuan Chen, Shuangyu Xie, Uksang Yoo, Xinyue Zhu, Cael Magner, Alvin Zhu, Daehwa Kim, Eric Heiden, Erwin Coumans, Miles Macklin, Spencer Huang, Ken Goldberg Late-breaking results program ↗ 5:00–6:30 PMCST Posters Poster Session II JW Marriott Poster 7 DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors Pengcheng Wang, Kaiwen Hong, Chensheng Peng, Katherine Rose Driggs-Campbell, Masayoshi Tomizuka, Chenfeng Xu, Chen Tang Paper ↗ Poster 39 Improving Robotic Generalist Policies via Flow Reversal Steering Andy Tang, William Chen, Andrew Wagenmaker, Chelsea Finn, Sergey Levine Paper ↗ Poster 54 EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data Ruijie Zheng, Dantong Niu, Yuqi Xie, Jing Wang, Mengda Xu, Yunfan Jiang, Fernando Castañeda, Fengyuan Hu, You Liang Tan, Letian Fu, Trevor Darrell, Furong Huang, Yuke Zhu, Danfei Xu, Linxi Fan Authors listed under Tuesday, Oral O5 Paper ↗ Poster 63 Flow Policy Gradients for Robot Control Brent Yi, Hongsuk Choi, Himanshu Gaurav Singh, Xiaoyu Huang, Takara E. Truong, Carmelo Sferrazza, Yi Ma, Rocky Duan, Pieter Abbeel, Guanya Shi, Karen Liu, Angjoo Kanazawa Authors listed under Tuesday, Oral O4 Paper ↗ Poster 77 Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control? Filippo Lazzati, Kyle Stachowicz, William Chen, Alberto Maria Metelli, Andrew Wagenmaker, Sergey Levine Paper ↗ Poster 142 FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation Chengbo Yuan, Zicheng Zhang, Mingjie Zhou, Wendi Chen, Yi Wang, Zhuoyang Liu, Dantong Niu, Shuo Wang, Hui Zhang, Wenkang Zhang, Yingdong Hu, Yuanqing Gong, Wanli Xing, Chuan Wen, Cewu Lu, Kaifeng Zhang, Yang Gao Paper ↗ Poster 153 PTLD: Sim-to-Real Privileged Tactile Latent Distillation for Dexterous Manipulation Rosy Chen, Mustafa Mukadam, Michael Kaess, Tingfan Wu, Francois Robert Hogan, Jitendra Malik, Akash Sharma Paper ↗ Poster 184 Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation Zihan Wang, Zhen Wu, Pieter Abbeel, Rocky Duan, Jitendra Malik, Carmelo Sferrazza, Karen Liu, Guanya Shi, Angjoo Kanazawa Paper ↗ Poster 189 VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes Yen-Jen Wang, Jiaman Li, Sirui Chen, Takara E. Truong, Pei Xu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Angjoo Kanazawa, Carmelo Sferrazza, Guanya Shi, Karen Liu Paper ↗ Poster 195 Social Humanoid: Socially Responsive Human–Humanoid Interaction Vongani Hlavutelo Maluleke, James Ni, Kie Horiuchi, Lea Wilken, Jiahui Lei, Evonne Ng, Roei Herzig, Jitendra Malik, Trevor Darrell, Angjoo Kanazawa Poster 217 SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz, Pieter Abbeel, Koushil Sreenath Authors listed under Tuesday, Oral O7 Paper ↗ Wednesday, November 11 Main conference12 papers 8:30–9:00 AMCST Spotlight · S6 Optimization, agentic planning and evaluation Hilton Austin Session chair: Negar Mehr Playful Agentic Robot Learning Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin, Justin Yu, Dantong Niu, Zirui Wang, Roei Herzig, Ken Goldberg, Yutong Bai, David M. Chan, Ion Stoica, Angjoo Kanazawa, Jiahui Lei, Haiwen Feng, Trevor Darrell Also at Poster Session III · 10:30 AM–12:00 PM · Poster 99 · JW Marriott Paper ↗ GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake, Daehwa Kim, Haoru Xue, Jonathan Francis, Christian Juette, Peter Schaldenbrand, Muhammet Yunus Seker, Ruwan Wickramarachchi, Uksang Yoo, Guanzhi Wang, Adithyavairavan Murali, Balakumar Sundaralingam, S. Shankar Sastry, Yuke Zhu, Linxi Fan, Spencer Huang, Ken Goldberg Also at Poster Session III · 10:30 AM–12:00 PM · Poster 100 · JW Marriott Paper ↗ 9:00–9:30 AMCST Spotlight · S7 Humanoid locomotion and whole-body control Hilton Austin LadderMan: Learning Humanoid Perceptive Ladder Climbing Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, Karen Liu, Guanya Shi Also at Poster Session III · 10:30 AM–12:00 PM · Poster 195 · JW Marriott Paper ↗ ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids Gechen Qu, Tong Zhang, Bike Zhang, Yen-Jen Wang, Koushil Sreenath, Claire Tomlin, Jason Jangho Choi Also at Poster Session III · 10:30 AM–12:00 PM · Poster 198 · JW Marriott Paper ↗ TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah Also at Poster Session III · 10:30 AM–12:00 PM · Poster 200 · JW Marriott Paper ↗ 10:30 AM–12:00 PMCST Posters Poster Session III JW Marriott Poster 51 Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Paper ↗ Poster 68 WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation Justin Yu, Andrew Goldberg, Kavish Kondap, Karim El-Refai, Ethan Ransing, Qianzhong Chen, Mac Schwager, Fred Shentu, Philipp Wu, Ken Goldberg Paper ↗ Poster 99 Playful Agentic Robot Learning Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu, Zihan Yang, Yaowei Liu, Raj Saravanan, Shaofeng Yin, Justin Yu, Dantong Niu, Zirui Wang, Roei Herzig, Ken Goldberg, Yutong Bai, David M. Chan, Ion Stoica, Angjoo Kanazawa, Jiahui Lei, Haiwen Feng, Trevor Darrell Authors listed under Wednesday, Spotlight S6 Paper ↗ Poster 100 GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks Kaiyuan Chen, Shuangyu Xie, Letian Fu, Justin Yu, William Pacini, Sandeep Bajamahal, Hudson Kim, Jaimyn Drake, Daehwa Kim, Haoru Xue, Jonathan Francis, Christian Juette, Peter Schaldenbrand, Muhammet Yunus Seker, Ruwan Wickramarachchi, Uksang Yoo, Guanzhi Wang, Adithyavairavan Murali, Balakumar Sundaralingam, S. Shankar Sastry, Yuke Zhu, Linxi Fan, Spencer Huang, Ken Goldberg Authors listed under Wednesday, Spotlight S6 Paper ↗ Poster 135 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Paper ↗ Poster 148 Tactile Curiosity Drives Robot Interaction Klemens Iten, Alexander Proshkin, Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza Paper ↗ Poster 159 RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains Yuanhang Zhang, Younggyo Seo, Juyue Chen, Yifu Yuan, Koushil Sreenath, Pieter Abbeel, Carmelo Sferrazza, Karen Liu, Rocky Duan, Guanya Shi Paper ↗ Poster 175 TEXEDO: Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation Jianuo Cao, Yuxin Chen, Yuzhen Song, Masayoshi Tomizuka, Chenran Li, Thomas Tian Paper ↗ Poster 178 CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning Yuxin Chen, Hari Srikanth, Nathan Jew, Menglin Wu, Pengcheng Wang, Junli Ren, Masayoshi Tomizuka, Peng Xu, Jinyu Xie, Thomas Tian Paper ↗ Poster 195 LadderMan: Learning Humanoid Perceptive Ladder Climbing Siheng Zhao, Yuanhang Zhang, Ziqi Lu, Pieter Abbeel, Rocky Duan, Koushil Sreenath, Yue Wang, Karen Liu, Guanya Shi Authors listed under Wednesday, Spotlight S7 Paper ↗ Poster 198 ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids Gechen Qu, Tong Zhang, Bike Zhang, Yen-Jen Wang, Koushil Sreenath, Claire Tomlin, Jason Jangho Choi Authors listed under Wednesday, Spotlight S7 Paper ↗ Poster 200 TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah Authors listed under Wednesday, Spotlight S7 Paper ↗ Thursday, November 12 WorkshopsTalks, papers and organizing roles 8:40–9:50 AMCST Workshop Roadmap to Sample-Efficient Real-World Reinforcement Learning (R2RL) · Keynote block JW Marriott · Room 213 Zhiyuan “Paul” Zhou Sergey Levine’s lab. Speaks during the keynote block. Session details ↗ 9:40–10:15 AMCST Workshop Bimanual Dexterous Tool Use · Spotlights JW Marriott · Rooms 303–304 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Authors listed under Wednesday, Poster Session III Paper ↗ Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Authors listed under Wednesday, Poster Session III Paper ↗ Workshop program ↗ 10:15–10:40 AMCST Workshop Bimanual Dexterous Tool Use · Posters & demos JW Marriott · Rooms 303–304 T-Rex: Tactile-Reactive Dexterous Manipulation Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle, Ruijie Zheng, Jing Wang, Ryan Punamiya, Mengda Xu, Yuqi Xie, Yunfan Jiang, Letian Fu, Konstantinos Kallidromitis, Matteo Gioia, Junyi Zhang, Jiaxin Ge, Haiwen Feng, Fabio Galasso, Wei Zhan, David M. Chan, Yutong Bai, Roei Herzig, Jiahui Lei, Fei-Fei Li, Ken Goldberg, Jitendra Malik, Pieter Abbeel, Yuke Zhu, Danfei Xu, Linxi Fan, Trevor Darrell Authors listed under Wednesday, Poster Session III Paper ↗ Do as I Do: Dexterous Manipulation Data from Everyday Human Videos Bhawna Paliwal, Haritheja Etukuru, William Liang, Pieter Abbeel, Nur Muhammad Mahi Shafiullah, Jitendra Malik Authors listed under Wednesday, Poster Session III Paper ↗ Workshop program ↗ 10:35–11:05 AMCST Workshop Everything Beneath the Policy · Posters Hilton Austin · Salon E, 4th floor TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning Dayou Li, Hao Wang, Qianqian Yang, Zihao Zhu, Haoquan Fang, Ziyao Zeng, Yan Han, Zihan Wang, Yan Wang, Baoru Huang, Dilin Wang, Kenji Shimada, Yiyue Luo, Manling Li, Teresa Lv, Mustafa Mukadam, Rakesh Ranjan, Ruohan Zhang, Qi He, Changliu Liu, Xu Chen, Marco Pavone, Bangya Liu, Jiachen Li, Masayoshi Tomizuka, Zhiwen Fan Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data Songbo Hu, Qiayuan Liao, Yufeng Chi, Kevin Zakka, Sophia Shao, Pieter Abbeel, Koushil Sreenath Paper ↗ Workshop program ↗ 10:40–11:10 AMCST Workshop Bimanual Dexterous Tool Use · Invited talk JW Marriott · Rooms 303–304 Professor Jitendra Malik Talk title has not been announced. Session details ↗ 11:50 AM–12:20 PMCST Workshop R2RL · Panel discussion JW Marriott · Room 213 Zhiyuan “Paul” Zhou Panel with workshop speakers and audience volunteers. Session details ↗ 2:45–3:05 PMCST Workshop Agentic Robotics · Invited talk Room to be announced Professor Ken Goldberg Talk title has not been announced. Session details ↗ Approx. 2:55–3:25 PMCST Workshop Sim-to-Real-to-Field · Invited talks Hilton Austin · Salon E, 4th floor Professor Claire Tomlin Shared invited-talk block with Michael Lutter (Boston Dynamics). Session details ↗ 4:15–5:55 PMCST Workshop Agentic Robotics · Live demos & posters Room to be announced ASPIRE: Agentic /Skills Discovery for Robotics Runyu Lu, Yubo Wu, Ethan Kou, Haotian Lin, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Jan Kautz, Yuke Zhu, Linxi Fan, Guanzhi Wang Agentic World Action Models Zhuoyang Liu, Dantong Niu, Botong Zhang, Boning Shao, Jing Wang, Ruijie Zheng, Yuvan Sharma, Runyu Lu, Jiaming Liu, Renrui Zhang, Roei Herzig, Yutong Bai, Ken Goldberg, Danfei Xu, Yuke Zhu, Guanzhi Wang, Linxi Fan, Trevor Darrell Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi Authors listed under Monday, Late-breaking results, Agentic Robotics Workshop program ↗ Additional involvement Workshop organizers BAIR behind the program Workshop organizing roles. Do Robots Need World Models? Lasse Peters, Negar Mehr’s lab — organizer. Sim-to-Real-to-Field S. Shankar Sastry, Bike Zhang, Siming He, Jonas Frey — organizers. “Oops, I Erred” Sergey Levine — faculty advisor. Zhiyuan “Paul” Zhou and Renhao Wang — organizers. Dexterous Manipulation: Full-Stack Robotics on Learning and Design Jitendra Malik — organizer. Workshop: 2:00–6:00 PM. About this schedule Schedule compiled from the conference program, paper and project pages, and workshop programs. Workshop schedules may change; consult each linked program for updates. Coverage includes 33 main-conference papers, 4 late-breaking results posters and the workshop contributions listed above. The main conference demo track is not covered. Main-conference poster numbers follow an unofficial paper index; check the official program on the day. Hilton stage content will also be streamed to viewing rooms at the Marriott. Paper affiliations reviewed November 6, 2026; late-breaking results updated November 8, 2026. Official CoRL program ↗BAIR faculty ↗Unofficial paper & poster index ↗Back to top ↑ 0; status.textContent = (!search.value && !type.value) ? "Showing all sessions" : visible + " matching session" + (visible === 1 ? "" : "s"); } function reset() { search.value = ""; type.value = ""; filter(); } search.addEventListener("input", filter); type.addEventListener("change", filter); document.getElementById("corl-reset").addEventListener("click", function () { reset(); search.focus(); }); Array.prototype.forEach.call(root.querySelectorAll(".corl-nav a, .corl-see a"), function (a) { a.addEventListener("click", reset); }); window.addEventListener("beforeprint", reset); })();

15 hours ago • 1 votes
AI and Irontown

For our last movie night we watched Princess Mononoke. I remembered loving that movie when I was younger, but watching it as an adult made me once again realize how Miyazaki has a way of expressing diverging sides without picking one. The film shows people, animals and spirits with legitimate and largely incompatible needs, and each faction is further engaged in infighting. A central arc of the story is Lady Eboshi’s Irontown, which gives people who were largely rejected by society work, protection and dignity. She gives shelter and work to those who have leprosy and to former sex workers. The mine and the weapon factory provide them with a livelihood, but they also destroy the forest around them. The forest’s inhabitants, in particular the boars, response choose war against the humans to defend themselves. Eboshi is not portrayed as a villain. She does real good for the people of Irontown, but also is an arms manufacturer who is destroying the forest around it. The film anchors around a character called Ashitaka, who has been condemned to death by an unfortunate encounter with a god that was turned into a demon after being fatally wounded by one of Eboshi’s guns. He doesn’t take sides, but he wants to understand and find the truth. Another character is San (whom they call Mononoke), a girl who was raised by wolves and, despite being human, considers herself part of the forest. This movie’s backdrop is human progress, and it reminded me so much of the challenges we are facing today with AI. AI is useful, empowering and consequential beyond the people who benefit from it. It’s similar to the iron in the story. Irontown gives the outcast purpose and power. There are people today who see themselves in an environment just like that: people who were never let in before and who can now build things that were out of reach. They are wielding AI to their benefit. But there are negative externalities. In Mononoke it’s not just the spirits and animals in the forest who bear the consequences, it’s also other people. Irontown produces guns, and its people are engaged in fights with other humans. There is conflict and power seeking everywhere. Another, unseen character is the power-hungry emperor, who wants the head of the forest spirit, a god, because he believes it will make him immortal. Eboshi is willing to support that cause, not because she thinks it’s right, but because she is at war with the forest’s gods who are preventing her expansion, so they have a common interest. But there really is no need for anyone to kill the forest spirit, in the same way there is no need for any one country to “win at AI”. What drives it is the fear of what happens if someone else wins first. And the emperor is willing to have a god killed for it. The people who help him with that desire, Eboshi included, don’t understand the chaos it will unleash. There it also shows the dangers of seeking overwhelming power without comprehension of how to control its consequences. The pursuit of power itself becomes destructive. In the end, the gods die, and what grows back is not the old forest anymore. Irontown is destroyed. Eboshi wants to rebuild it, and to build a better town this time. Ashitaka stays with the people of Irontown after his curse is lifted by the dying forest spirit. San cannot forgive the humans and stays with the wolves. In the end, nothing is truly resolved, but there is a truce that came at great cost. Ashitaka’s role in the movie is one of a neutral actor. He was wronged both by a spirit turned demon in the forest, who put a curse on him, and, indirectly, by Eboshi, who caused the spirit to turn evil. Yet he harbors no ill will towards either side. He sets out to “see with eyes unclouded by hate,” and in the course of the movie he is seen as an unaligned actor. He helps people on both sides, and stands in their way to prevent further conflict. He doesn’t feel like a pacifist, but he’s the closest to one there is. He’s a truth seeker in a changing world. Because he engages he’s seen as suspicious by both sides, and ends up shot by the people of Irontown when he tries to rescue San. I use AI every day and find it genuinely empowering. But I also recognize the pain it causes some, and the moral problems it poses. It’s genuine progress for humanity, but it comes at significant cost. And many of those costs will be carried by people who aren’t going to benefit from it. So it’s no surprise that AI feels like a threat to many today. Communities currently strongly align themselves with one side and fight against the other. The forst spirit did not have to die and the boars did not have to die in a senseless war. The question is not whether there will be iron, but what kind of Irontown we build with it. I want our conversation about AI to make room for nuance and genuine understanding, for us to look at it and each other with eyes unclouded by hate. We should take advantage of its real benefits, but also recognize the damage it can do, and the choices we still have about what we build and what we’re willing to sacrifice for it. I like to think there is a path for people with differing opinions can find a way to live alongside before losing something we can’t get back.

yesterday • 1 votes
The Token Fed

Towards greater financialisation of the token economy

2 days ago • 1 votes
Estimating the Value of AppSec Methods

Really excited to share some work we did with Xint.io. We don’t always get to publicly share what we do. We have permission to share this one, and it is some public good. Xint.io provides a code vulnerability scan service that helps flag Common Vulnerabilities and Exposures (CVEs) in a […]

4 days ago • 1 votes
📚 BoredReading

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