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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/…
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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
27 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.
13 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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Simon LC retweeted
12 Dec 2024
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
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Simon LC retweeted
7 Nov 2024
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!
4 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
6 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…
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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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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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Simon LC retweeted
10 Oct 2024
Excited to share our new📰, DROP: Dexterous Reorientation via Online Planning!
Overview:
🔹We tackle cube rotation🧊♻️on hardware
🔹DROP is the first 🧊♻️sampling-based MPC demo. No reinforcement learning!
🔹Median 30.5 rotations w/o dropping, max of 81👑🦾
See 🧵below👇
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Really cool work on bimanual dexterity!!
Achieving bimanual dexterity with RL Sim2Real!
toruowo.github.io/bimanual-t…
TLDR - We train two robot hands to twist bottle lids using deep RL followed by sim-to-real. A single policy trained with simple simulated bottles can generalize to drastically different real-world objects.
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Simon LC retweeted
29 Jan 2024
Excited to share our latest work, Vision-Language Frontier Maps – a SOTA approach for semantic navigation in robotics. VLFM enables robots to navigate and find objects in novel environments using vision-language foundation models, zero-shot! Accepted to #ICRA2024!
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Simon LC retweeted
20 Jan 2024
want your next robot 🤖 to move like your favorite pet 🐶? youtube videos might be all you need.
checkout our recent work -- SLoMo
paper: rexlab.ri.cmu.edu/papers/slo…
website: slomo-www.github.io/website/
video: youtu.be/bvoM-nBd7lM?si=9f2g…
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