Cheng Chi @chichengcc
š¤PhD student @Stanford and @Columbia cheng-chi.github.io Stanford, CA Joined January 2016-
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š š¢šµāš«Tired of endless data collection & fine-tuning every time you try out VLA? Meet RDT2, the first foundation model that zero-shot deploys on any robot arms with unseen scenes, objects & instructions. No collection. No tuning. Just plug and playš Witness a clear sign ofā¦
How do we unlock the full dexterity of robot hands with data, even beyond what teleoperation can achieve? DEXOP captures natural human manipulation with full-hand tactile & proprio sensing, plus direct force feedback to users, without needing a robotšdex-op.github.io
I was really impressed by the UMI gripper (@chichengcc et al.), but a key limitation is that **force-related data wasnāt captured**: humans feel haptic feedback through the mechanical springs, but the robot couldnāt leverage that info, limiting the dataās value for fine-grainedā¦
I was really impressed by the UMI gripper (@chichengcc et al.), but a key limitation is that **force-related data wasnāt captured**: humans feel haptic feedback through the mechanical springs, but the robot couldnāt leverage that info, limiting the dataās value for fine-grainedā¦ https://t.co/0BvUVp66kq
Normally, changing robot policy behavior means changing its weights or relying on a goal-conditioned policy. What if there was another way? Check out DynaGuide, a novel policy steering approach that works on any pretrained diffusion policy. dynaguide.github.io š§µ
Say ahoy to šš°šøš»š¾šāµ: a new paradigm of *learning to search* from demonstrations, enabling test-time reasoning about how to recover from mistakes w/o any additional human feedback! šš°šøš»š¾š āµ out-performs Diffusion Policies trained via behavioral cloning on 5-10x data!
Diffusion/flow policies š¤ sample a ātrajectory of trajectoriesā ā a diffusion/flow trajectory of action trajectories. Seems wasteful? Presenting Streaming Flow Policy that simplifies and speeds up diffusion/flow policies by treating action trajectories as flow trajectories! šā¦
Are Diffusion and Flow Matching the best generative modelling algorithms for behaviour cloning in robotics? ā Multimodality āFast, Single-Step Inference āSample Efficient š” We introduce IMLE Policy, a novel behaviour cloning approach that can satisfy all the above. š§µš
Meet the newest member of the UMI family: DexUMI! Designed for intuitive data collection ā and it fixes a few things the original UMI couldnāt handle: šļø Supports multi-finger dexterous hands ā tested on both under- and fully-actuated types š§ Records tactile info ā it can tellā¦
Meet the newest member of the UMI family: DexUMI! Designed for intuitive data collection ā and it fixes a few things the original UMI couldnāt handle: šļø Supports multi-finger dexterous hands ā tested on both under- and fully-actuated types š§ Records tactile info ā it can tellā¦
Can we collect robot dexterous hand data directly with human hand? Introducing DexUMI: 0 teleoperation and 0 re-targeting dexterous hand data collection system ā autonomously complete precise, long-horizon and contact-rich tasks Project Page: dex-umi.github.io
Training robots for the open world needs diverse data But collecting robot demos in the wild is hard! Presenting DexWild ššļø Human data collection system that works in diverse environments, without robots šŖš¦¾ Human + Robot Cotraining pipeline that unlocks generalization š§µš
š¦¾How can humanoids unlock real strength for heavy-duty loco-manipulation? Meet FALCONš¦ : Learning Force-Adaptive Humanoid Loco-Manipulation. š: lecar-lab.github.io/falcon-humanoiā¦ See the details belowš:
š¤Introducing TWIST: Teleoperated Whole-Body Imitation System. We develop a humanoid teleoperation system to enable coordinated, versatile, whole-body movements, using a single neural network. This is our first step toward general-purpose robots. šhumanoid-teleop.github.io
Low-cost teleop systems have democratized robot data collection, but they lack any force feedback, making it challenging to teleoperate contact-rich tasks. Many robot arms provide force information ā a critical yet underutilized modality in robot learning. We introduce: 1. š¦¾Aā¦
Two months ago, we introduced TidyBot++, our open-source mobile manipulator. Today, I'm excited to share our significantly expanded docs: ā¢ Assembly guide: tidybot2.github.io/docs ā¢ Usage guide: tidybot2.github.io/docs/usage Thanks to early adopters, TidyBot++ can now be fullyā¦
Two months ago, we introduced TidyBot++, our open-source mobile manipulator. Today, I'm excited to share our significantly expanded docs: ā¢ Assembly guide: tidybot2.github.io/docs ā¢ Usage guide: tidybot2.github.io/docs/usage Thanks to early adopters, TidyBot++ can now be fullyā¦ https://t.co/jDiYHOgFHl
Happy Valentines Day! š¹ Enjoy a special Valentine's day themed policy (sound on!) from the AquaBot team š¬ā¤ļøš¦¾ Visit aquabot.cs.columbia.edu to learn more about our recent ICRA publication!
Announcing Diffusion Forcing Transformer (DFoT), our new video diffusion algorithm that generates ultra-long videos of 800+ frames. DFoT enables History Guidance, a simple add-on to any existing video diffusion models for a quality boost. Website: boyuan.space/history-guidanā¦ (1/7)
We introduce Dexterity Gen (DexGen), a foundation controller that enables unprecedented dexterous manipulation capabilities. For the first time, it allows human teleoperation of tasks such as using a pen, screwdriver, and syringe. Developed by @berkeley_ai and @metaai. A Thread.
Excited to introduce flow Q-learning (FQL)! Flow Q-learning is a *simple* and scalable data-driven RL method that trains an expressive policy with flow matching. Paper: arxiv.org/abs/2502.02538 Project page: seohong.me/projects/fql/ Thread ā
We are releasing the Ļā model today -- code + weights + fine-tuning instructions, including our recent Ļā-FAST model! š We hope the model will be useful to others! I am really excited about this release because it also marks a shift in how we can *evaluate* policies! Mini š§µ/
We are releasing the Ļā model today -- code + weights + fine-tuning instructions, including our recent Ļā-FAST model! š We hope the model will be useful to others! I am really excited about this release because it also marks a shift in how we can *evaluate* policies! Mini š§µ/
š Meet ToddlerBot š¤ā the adorable, low-cost, open-source humanoid anyone can build, use, and repair! Weāre making everything open-source & hope to see more Toddys out there!
š Meet ToddlerBot š¤ā the adorable, low-cost, open-source humanoid anyone can build, use, and repair! Weāre making everything open-source & hope to see more Toddys out there!
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