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Simon LC (@simonlc_) profile picture

Simon LC

@simonlc_

research scientist @ the AI institute robotics & optimization phd @ stanford

 New York
Joined September 2019
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Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)Photo by Simon LC (@simonlc_)
Excited to share our latest project on vision-based dynamic manipulation at RAI Institute! We took Spot outside the lab: it now manipulates objects at speed, fully autonomously, using only its onboard sensors and computer. 🧵 Youtube: youtube.com/watch?v=nDIWulWj…
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Fully onboard: five depth cameras and one Mac Mini mounted on the robot’s back. Tested indoors and outdoors, including a skatepark, a basketball court, and a garage.
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Great teamwork with amazing colleagues on this one: Leonor Fermoselle (@l_fermoselle), Maks Sorokin (@itsmaksX) Duy Ta (@thduynguyen), Dmitry Yershov, Farzad Niroui (@B31FN), Brandon Hung, Jan Brüdigam, Emmanuel Panov. Thrilled to finally put it out!
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Excited to share what I've been working over the last few months! We taught Spot to lift, roll, and stack tires completely autonomously. It uses its arms, body, and legs to manipulate heavy objects at speed.
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We also show RL-based tire uprighting, where the tire literally flies into the air. This illustrates just how dynamic whole-body manipulation can be.
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If you're interested in working on whole-body manipulation at @rai_inst feel free to use this link jobs.lever.co/rai?lever-via=… Check out the blog post for more details: rai-inst.com/resources/blog/…
Robotics and AI Institute

Robotics and AI Institute

Job openings at Robotics and AI Institute

jobs.lever.co
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I am really happy to finally share this work! Huge congrats to @albert_h_li and @pdculbert for the effort they put in making the tool hackable and interactive. Try it!🐍 pip install judo-rai
Preston Culbertson (@pdculbert) profile picturePreston Culbertson@pdculbert27 Jun 2025
🥋 We're excited to share judo: a hackable toolbox for sampling-based MPC (SMPC), data collection, and more, designed to make it easier to experiment with high-performance control. Try it: pip install judo-rai
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Our new paper shows how task representations learned via temporal alignment enable compositional generalization for conditional policies. This allows robots to solve compound tasks by implicitly decomposing them into subtasks.
Vivek Myers (@vivek_myers) profile pictureVivek Myers@vivek_myers13 Feb 2025
Current robot learning methods are good at imitating tasks seen during training, but struggle to compose behaviors in new ways. When training imitation policies, we found something surprising—using temporally-aligned task representations enabled compositional generalization. 1/
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Learning bimanual, contact-rich robot manipulation policies that generalize over diverse objects has long been a challenge. Excited to share our work: Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation! glide-manip.github.io 🧵1/n
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ICYMI: For #CoRL2024 we released a dataset of 3.5M (!) dexterous grasps, with multi-trial labels and perceptual data for 4.3k objects. Our takeaways: scale matters, and refining grasps > better sampling. Hoping our data can enable more vision-based grasps in hardware!
Albert Li (@albert_h_li) profile pictureAlbert Li@albert_h_li4 Nov 2024
There have been many recent big grasping datasets, but few demos of real-world grasping using generative models. How do we achieve this? Introducing: Get a Grip (#corl2024)! We show that instead of generative models, discriminative models can attain sim2real transfer! 👀🧵👇
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We're presenting Jacta: a versatile planner for learning dexterous and whole-body manipulation this week at CoRL! website jacta-manipulation.github.io… paper arxiv.org/abs/2408.01258
RAI Institute (@rai_inst) profile pictureRAI Institute
@rai_inst6 Nov 2024
Our team is presenting work at the Conference on Robot Learning, @corl_conf, in Munich, Germany this week! Learn more about our accepted research — theaiinstitute.com/news/corl…
CoRL 2024 Publication Round-Up | RAI Institute

CoRL 2024 Publication Round-Up | RAI Institute

The annual Conference on Robot Learning (CoRL) takes place this week, November 6-9 in Munich, Germany. Read on to learn more about our accepted publications.

rai-inst.com
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I'm excited to share Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation. We use sampling-based planning to bootstrap policy learning methods for manipulation tasks. My friend, Jan Brüdigam is presenting the work today at CoRL! jacta-manipulation.github.io…
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We used reinforcement learning bootstrapped with expert planner demonstrations to learn robust policies. We deployed them on several hardware scenarios using Boston Dynamics' Spot
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This is a joint work with an amazing team! Jan Brüdigam, Ali Abbas, @initmaks, @KuanFang, Brandon Hung, Maya Guru, Stefan Sosnowski, Jiuguang Wang and Sandra Hirche Jan did an awesome job leading the project during his internship at @the_ai_inst. So proud of what we accomplished!
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