Full Width 
Shortcuts 
Sign Up 
Log In 
Articles 
Reading List More from matt.sh
agent-shell: stop spilling your outputs since the beginning of time, ai agents have wanted one thing at one thing only: redirecting stderr to stdout then piping the output to head or tail or grep or sed or awk. That’s all they really want. Except, they do it wrong the majority of the time (because “ai labs” are fundamentally incompetent at software engineering, as we’ve previously discussed before). Common ones I see routinely which are painful and require manual “reminders” into the agent to stop: uv run my-big-honking-test 2>&1 | grep -E "failed:|error:" | head -5 but then the test fails with a non-zero error code, the grep reports an error count, so then the agent RE-RUNS THE ENTIRE TEST with a different grep failure to “find the failing test case” except, the failing test case grep requires “guessing the future” regex, but the first attempt at “guessing the error regex” fails, then code agent continues re-running a 20 minute test suite with different grep filters until you remind it: “FILES EXIST! WE HAVE A FILE SYSTEM! WRITE OUTPUT TO FILES THEN READ THE FILES TO SEE THE ERRORS!” also, since current ai code agents have no feeling for “time” or “effort” they don’t care about running a 30 minute program-stderr-to-stdout-to-pipe-grep 10 times in a row even if each attempt takes 30 minutes. and a more toxic approach is where models decide to use live public URLs for reference material then curl | grep except, again, models try to “guess grep filters”, fail, then re-run the curl a dozen times with different “guess the regex” filters instead of saving the content locally and then searching through it (I often see claude get rate limited or blocked by upstream services because it “curls and greps” the same URLs in a loop, and you have to remind it every time “hey, files exist? you know? use files?”) I’ve run some bulk analysis about how often models “do the wrong thing” or “waste time guessing” (instead of following more proof-based data practices) over my claude session history (you do know all your claude session and all commands and tool calls are saved at ~/.claude right?) and here’s results: ways models try to read things Pattern Count Share of Bash commands Piped into a filter (grep/head/tail/awk/sed/etc.) 123,637 62.8% Bare narrowing command, no pipe 22,963 11.7% – Any pipe at all 135,413 68.8% Bare full-file read (cat) 1,573 0.8% Redirect-to-file, later read in full (“save then read”) 4,301 — common filter patterns guesses: Shape Count Likely context sed (bare, e.g. sed -n 'N,Mp' file) 15,123 line-range paging grep | head 9,497 search, then cap the results cd | head 5,998 cd dir && cmd \| head grep (bare) 4,523 direct search on a file uv | tail 3,864 tailing output of a uv run/uv pip command cd | tail 3,253 cd dir && cmd \| tail tail (bare) 2,764 log/output tailing ls | head 1,699 capped directory listing find | head 1,365 capped file search common slop guess counts: Metric Count Guess-then-give-up-and-read episodes (≥2 narrow attempts, then a full read) 541 Unresolved thrash episodes (≥3 narrow attempts, never followed by a full read) 2,701 Total narrow commands spent inside these episodes 5,006 Resolved episodes closed by the Read tool 447 (83%) Resolved episodes closed by a bare cat 94 (17%) multiple different cmd | filter guesses in a row here are full attempt-count distribution for episodes eventually resolving into a full read (example: running a command with a failed filter, then continuing to run cmd | filterA -> cmd | filterB over and over again until a “filter” matches instead of just saving output to a file and reading directly):
img,video{width:100%} claude-hooker The mystery of claude code over the past year and a half has been how does a world leading ‘AI productization lab’ continually generate such shitty amatuer globally distributed software and services over and over again? It’s almost like staffing a company entirely by 24 year olds seeing their net worth increase by $2 million per month for two years is a bad idea? Even the tech brain infosphere has noticed how claude code’s architecture is absolutely mornoic and amatuer and anthropic just continually ego-sociopath lies and nobody can stop them because hey, when you spend over $2 billion a month renting GPUs run by globally polluting gas turbines, they let you do it. don’t worry, bro, “it’s a javascript game engine in your console, ur not smaht enouf to understand our god-ai logic here!!!” as you may be able to tell, i’m deploying this post in annoyance/anger mode because I don’t understand why me, somebody who can’t afford a place to live and has 20 years of detailed technical experience, is giving free work to trillion dollar “ai” “startups” with good models but apparently abysmally amatuer software engineering practices. i guess it’s part of industry bubble syndrome. It’s not really worth thinking about what drives weird un-self-aware ego-sociopathic-autistic billionaires if you aren’t close enough to change anything in their minds though. The Bad i guess back to the point: given they have made claude so same-system dangerous with its cavalier attitude towards running destructive or system-resource-consuming actions you must install your own safeguards using claude code event hooks.
Kvestigate - Investigate Kubernetes Clusters Ever find yourself given access to a piece of garbage kubernetes cluster and you need need to audit it to figure out what the hell is going on? Say hello to kvestigate your kubernetes investigation and auditing toolkit with the ability to even run ad-hoc workloads beyond the reach of the kubernetes scheduler allocator substrate system by just doing shit on the hosts themselves like computers are supposed to do. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Language Files Lines Code Comments Blanks ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Python 141 34192 28551 1381 4260 TOML 3 72 57 8 7 YAML 23 1059 718 257 84 ───────────────────────────────────────────────────────────────────────────────── Markdown 18 3983 0 3083 900 |- BASH 13 500 288 141 71 |- Python 6 177 132 27 18 |- YAML 3 218 187 11 20 (Total) 4878 607 3262 1009 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Total 185 40201 29933 4908 5360 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Features kvestigate is a read-first Kubernetes toolkit for investigating clusters, running workloads directly on host machines (no Docker), and safely handing a constrained slice of your cluster resources to AI agents. Three surfaces on one foundation (the official kubernetes client + a privileged node-access DaemonSet): Investigate & plan — audit a cluster, stream logs, exec into pods, and bin-pack a workload onto available GPU/CPU. Read-only by default. Run things host-native — create isolated workspaces on a node’s host OS (tmpfs / disk / hybrid storage, GPUs bound by UUID), sync code in, and launch processes. No container build, no pod scheduling. Host constrained cyberspaces — expose an allow-listed, quota’d, audited slice of those workspace capabilities over HTTP/MCP, so agents and other programs can “upload and run things” without cluster-wide power. Bonus Feature Natively integrates with nvidia-smi to query all nodes for cluster-wide gpu usage and power metrics to see how much of the world you are wasting with your “ai” “workloads”
More in AI
20 years ago to this day, I started my new technical blog. I'd had a personal one for some time, but most of the stuff that happened there had already migrated to social media. I wanted somewhere to post the things that would only be of interest to my techier friends, and maybe of some use to anyone else that came across it. Things have changed a bit, but they've stayed the same in some ways. Earlier this year I finished my 48-part (!) LLM from scratch series, but it had predecessors -- the third post I made here was part one of a 13-part series where I worked through setting up automated backups for my homelab NAS, and then later on I spent a few months building a reverse proxy in C, not to mention the eight-month, ten-post series on fine-tuning LLMs. It does seem like there's some value to putting all of this online. I rarely check my logs and referrers, but people do still seem to be arriving here having searched for stuff that I've written about, and every now and then there's a kind "thanks for this!" comment left on an old post where I explained something I'd just learned. I wrote a bit more about the blog's history last year, and came to the conclusion that the best posts -- both for me to write, and as far as I could tell for other people to read -- were the ones where I wrote up a project that I'd been working on. I blogged about that in more detail, and it seemed to strike a chord on Hacker News; I think it still holds. Recently, Sean Goedecke suggested that the best blog posts are those where you write about things that you're in the process of learning, and I agree. You learn more than you would if you didn't write it up, and because things are fresh in your mind, you may well write more clearly about it than an actual expert in the subject might have done. Anyway, I'm going to carry on blogging. I'm working on my biggest LLM-related project so far (dozens of training runs, so my study aircon is getting quite the workout even with the weather turning colder), and look forward to posting about it later on this month. I hope it's at least as fun to read as it is to write!
Announcing STABL: Societal Transformation and AI Bottlenecks Lab
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); })();
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.
