Learning and Intelligent Systems (LIS) @ MIT @MIT_LISLab
PIs: Leslie Pack Kaelbling, Tomás Lozano-Pérez AI/ML/Robotics Research @MIT_CSAIL Website: https://t.co/TgsPKmvPCb Joined November 2020-
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Exciting to see the launch of the NVIDIA Isaac GR00T Reference Humanoid Robot. Open, research-focused humanoid platforms can help accelerate progress across the robotics community. Looking forward to the innovations this enables. A great contribution from NVIDIA Robotics!
NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the @UnitreeRobotics H2 humanoid robot, @SharpaRobotics Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard
Excited to be at #ICRA2026 in Vienna this week 🇦🇹 -- I'll be presenting SCOUT as a spotlight talk at the Rigorous Robot Perception workshop on June 01 at VIP Lounge C, and SceneComplete as an Oral talk today (June 02 at TuAT3.5).
𝗧𝗶𝗣𝗧𝗼𝗣 𝗶𝘀 #𝟭 𝗼𝗻 𝗠𝗼𝗹𝗺𝗼𝗦𝗽𝗮𝗰𝗲𝘀! Outperforming VLAs including MolmoAct2 and π₀.₅, and WAMs like DreamZero It's the only method that uses inference-time search and 𝙯𝙚𝙧𝙤 robot data. We didn't do any benchmark-specific tuning.
We ran TiPToP on MolmoSpaces and achieved SOTA! And we wrote an X article thing to discuss details - excited to hear any questions/comments! Check out the leaderboard here: molmospaces.allen.ai/leaderboard. TiPToP is also fully-open and should take <1hr of setup: try it yourself here: tiptop-robot.readthedocs.io/en/latest/
Congrats to the newest PhD grad from the lab @nishanthkumar23 🤖
PhDone and officially a robot doctor! Thanks to incredible advisors Leslie and Tomás @MIT_LISLab and my secret bonus advisor @tomssilver - I’m so so lucky to have gotten to do research with them. I’m excited to keep working on making useful generalist robots a reality 🤖🚀
We've released the TiPToP code open source! 🚀 We put a lot of effort into making this easy to run, install and build on. You can get it running on your DROID setup in under an hour. No training data needed. No demonstrations. Just type in what you want your robot to do 🤖💬 You can also adapt it to new embodiments with minimal effort. Give it a try and let us know what you think! 💻 Code: github.com/tiptop-robot/t… 📖 Docs: tiptop-robot.readthedocs.io
State-of-the-art robot policies often need hundreds of hours of data. What if we needed none? Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).
Imagine you told a robot to "find your car keys" in your apartment and it looked around, opened a drawer, and retrieved them for you. As a step towards that, I adapted TiPToP to run on the RBY1 humanoid in our lab! Here's an example instruction it follows: "Put the green block on the blue plate and the yellow block on the magazine." TiPToP helps plan over the right arm + single torso joint, but it's easy to unlock more joints -- even the base wheels -- for more expressive, real-world tasks. Humans find objects without thinking twice. One day, robots will too! 🤖
State-of-the-art robot policies often need hundreds of hours of data. What if we needed none? Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).
Everyone is scaling VLAs with more robot data. TiPToP shows another path. No robot training, no policy learning. Just RGB + language → 3D scene → GPU TAMP planner → trajectory. Foundation models + planning alone can run real manipulation tasks.
Check out a new robot manipulation system from members of the group combining foundation models with TAMP!
State-of-the-art robot policies often need hundreds of hours of data. What if we needed none? Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).
It was a pleasure to be back at @MIT to present at the #Robotics Seminar! Great to see all the exciting work happening there. Thanks so much @GioeleZardini for hosting me!
Data Flywheel -> Data Avalanche: Thx to Leslie Kaebling and @pulkitology Agrawal for suggesting "avalanche" as a better metaphor than "flywheel" for combining model-free + model-based methods to bootstrap a specific robot task to bootstrap & amplify on-policy data collection.
Check out new work from @nishanthkumar23 in the group on learning and planning with symbolic world models!
Reasoning over long horizons would allow robots to generalize better to unseen environments and settings zero-shot. One mechanism for this kind of reasoning would be world models, but traditional video world models still tend to struggle with long horizons, and are very data
tune in soon for a podcast episode covering new work from the group!
Full episode dropping soon! Geeking out with @nishanthkumar23 on From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models pix2pred.csail.mit.edu Co-hosted by @micoolcho @chris_j_paxton
Check out new work from the group on data-efficient learning of symbolic world models!
World models hold a lot of promise for robotics, but they're data hungry and often struggle with long horizons. We learn models from a few (< 10) human demos that enable a robot to plan in completely novel scenes! Our key idea is to model *symbols* not pixels 👇
Check out Leslie Kaelbling's #RLC2025 Keynote where she talks about some new pespectives and a number of new works from the group: youtube.com/watch?v=10OjAI…
We're excited for #RLC2025! If you're at the conference, be sure to catch our PI Leslie Kaelbling's keynote on "RL: Rational Learning" from 9-10 in CCIS 1-430. Leslie will talk about some new perspectives + exciting new results from the group: you won't want to miss it! 🤖
#ICRA2025 🤖 I spent 3 years of PhD making efficient long-horizon manipulation planning algorithms. VLMs ultimately provide the essential common-sense and horizon-reduction benefits. ❗VLMs can generate plausible robot task plans, but actions may not be feasible for robots due to reachability and obstacles. 🤝 So we use a TAMP planner to take in the next VLM-generated subgoal and plan for actions and trajectories. 📈 VLM-TAMP successfully solves cooking problems that require 30-50 actions and interact with 20+ objects. Excited to present it on Thursday! zt-yang.github.io/vlm-tamp-robot/
Check out new coverage by @MIT_CSAIL of our lab members' recent work!
Can we teach a robot its limits to do chores safely & correctly? 🧵 To help robots execute open-ended, multi-step tasks, MIT CSAIL researchers used vision models to see what’s near the machine & model its constraints. An LLM sketches up a plan that’s checked in a simulator to
Curious to hear about creating generalist robots from leaders in the field? Don’t miss our panel “Representations for Generalist Robots” (4-5pm) @corl_conf LEAP workshop! Feat. @chelseabfinn @animesh_garg Vincent Vanhoucke @Marc__Toussaint @sidsrivast and Leslie Kaelbling!
Check out new work on scene completion and grasping from a single RGB-D image!
🚀Excited to share SceneComplete: an open-world 3D scene completion system for constructing a complete, segmented 3D model of a scene from a single RGB-D image.🖼️🤖 SceneComplete enables dexterous grasping and robust robot manipulation in highly cluttered scenes - a short 🧵
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Eric Rosen @_ericrosen
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Turgut @pmaverick082
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teddy sovereign @tmistmm
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Alejandro @asalinasdl
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bm rayan ✈️ CVPR ... @MRBarhdadi
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sidcode @sidcode_
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Xiao @Alex_Xiao_
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Evan Liu @evan123liu
134 Followers 444 Following Curious About Learning the Universe | UW-Madison 2028 Founded @AgenticAIUW
Daniel Abejoye @senate_abejoye
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Leo Kozachkov @Leokoz8
1K Followers 571 Following Doing cybernetics (without being allowed to call it that). Assistant Professor @BrownUniversity. Prev: @IBMResearch @MIT, @RutgersU
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Zhi (Leo) Wang @TX_Leo_Wang
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YEdawu66 @PFo32udr
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Kun Tao @KunTao221218
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Vaibhav Wanere @vbwanere
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Jesse Silverberg @SilverbergJesse
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kld61987 @KLD61987
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Art Kanke @ArtKanke
55 Followers 495 Following
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3 Followers 180 Following BSc Mechatronic Engineering @ PUCP Research @ NONHUMAN. https://t.co/ynYvRNDlaC
Yann LeCun @ylecun
1.2M Followers 788 Following Professor at NYU & Executive Chairman at AMI Labs. Ex-Chief AI Scientist at Meta. Researcher in AI, Machine Learning, Robotics, etc. ACM Turing Award Laureate.
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34K Followers 1K Following Foundation Models for Generalizable Autonomy in Robotics. Reinforcement Learning. Faculty in AI Robotics @GeorgiaTech. Prev @nvidia @UCberkeley @stanford
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692K Followers 826 Following Co-founder @ndea. Co-founder @arcprize. Creator of Keras and ARC-AGI. Author of 'Deep Learning with Python'.
Google DeepMind @GoogleDeepMind
1.4M Followers 279 Following The engine room of @Google. Building AI safely and responsibly to solve the world’s most complex problems. Join us: https://t.co/jUHQA27iBL
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Kosta Derpanis (sabba... @CSProfKGD
81K Followers 199 Following #CS Associate Prof @YorkUniversity, #ComputerVision Researcher, @VectorInst Affiliate, TPAMI AE, @ELLISforEurope Member #CVPR2026 #ECCV2026 Publicity Chair
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64K Followers 59 Following Student of mind and nature, libertarian, chess player, cancer survivor. @ Keen, UAlberta, Amii, https://t.co/u8za2Kod54, The Royal Society, Turing Award
Conference on Robot L... @corl_conf
12K Followers 46 Following Conference on Robot Learning (CoRL) is an annual international conference on robotics and machine learning.
Ken Goldberg @Ken_Goldberg
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Gautam Kamath @thegautamkamath
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Chris Paxton @chris_j_paxton
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Luca Carlone @lucacarlone1
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MIT CSAIL Alliances @csail_alliances
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Josh Roy @thosehippos
76 Followers 246 Following Passionate about using technology to change the world! Ex-Two Sigma, Ex-Cognex M.S., B.S. Brown University
Kamyar Ghasemipour @coolboi95
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Felix Wang @felixwyw
881 Followers 509 Following Member of Technical Staff @GeneralistAI_ PhD @MIT_CSAIL
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326 Followers 44 Following researching robots @MIT_LISLab and building @tryinstance MIT
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Jack Lindsey @Jack_W_Lindsey
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Annie Chen @_anniechen_
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110 Followers 354 Following PhD at @MITEECS in the @AlgAlignMIT on alignment for Human-AI Agent/Robot collaboration and embodied agents, pianist | Prev @BrownBigAI @BrownUniversity '23
RoboPapers @RoboPapers
5K Followers 3 Following @chris_j_paxton, @micoolcho & @DJiafei geeking out weekly with authors of robotics AI papers. On YouTube / X / Spotify / Substack
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RL_Conference @RL_Conference
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Yuandong Tian @tydsh
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Yoonchang Sung @yoonchangsung
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ARC Prize @arcprize
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Sense Think Act Podca... @sense_think_act
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Muddy Charles Pub @muddycharlespub
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Andreea Bobu @andreea7b
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Wenlong Huang @ CVPR @wenlong_huang
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Subbarao Kambhampati ... @rao2z
28K Followers 72 Following AI researcher & teacher @SCAI_ASU. Former President of @RealAAAI; Chair of @AAAS Sec T. Here to tweach #AI. YouTube Ch: https://t.co/4beUPOmf6y Bsky: rao2z
Hao Su Lab @HaoSuLabUCSD
3K Followers 212 Following Researching at the frontier of AI on topics of Computer Vision, Computer Graphics, Robotics, Embodied AI, and Reinforcement Learning @UCSanDiego @haosu_twitr
Shuang Li @ShuangL13799063
8K Followers 1K Following Research Scientist @GoogleDeepMind. Working on Robotics and Omni Models. prev. @Stanford @MIT
Eric Rosen @_ericrosen
1K Followers 789 Following Currently: Something new… 👀 | Prev: @rai_inst, CS PhD from @BrownUniversity 🤖
Aditya Agarwal @skymanaditya1
686 Followers 250 Following PhD student @MIT | Robotics Research @MIT_LISLab
Zhutian (Skye) Yang @ZhutianYang_
1K Followers 211 Following Robotics & AI at @GoogleDeepMind. PhD from @MIT_CSAIL. Ex-Intern at @NVIDIAAI @ToyotaResearch. Opinions are my own.
Ryan Adams @ryan_p_adams
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680 Followers 3K Following Postdoctoral Researcher @Stanford | Decision-making on 🤖 robots and agents. | Prev Northeastern, Meta, BDAI, Amazon, Microsoft, UCSD.Trends for United States
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