Helping Hands Lab @ Northeastern
@HelpingHandsLab
🤖 Robotic Manipulation | Reinforcement Learning | PI Rob Platt @RobotPlatt @KhouryCollege @Northeastern
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#CoRL2024 Equivariant Diffusion Policy Led by @Dian_Wang_. A sample efficient BC algorithm based on equi diffusion. It leverages symmetry to boost learning with 5x less training data and mastering complex tasks with <60 demos. Presenting at Oral Session 1 and Poster Session 2.
Our new #ICML2025 paper, led by @ZhaoHaibo47588, presents a hierarchical equivariance architecture that enables multi-level sample efficiency in visuomotor policy learning. Check it out for more details!
Excited to share our #ICML2025 paper, Hierarchical Equivariant Policy via Frame Transfer. Our Frame Transfer interface imposes high-level decision as a coordinate frame change in the low-level, boosting sim performance by 20%+ and enabling complex manipulation with 30 demos.
Honored to receive my first best paper nomination from @corl_conf! Had such a great time at #CoRL2024, huge thanks to the organizing committee and all my co-authors: Steve, David, @tarikkelestemur, @HaojieHuang13, @ZhaoHaibo47588, Mark, JW, @RobinSFWalters, @RobotPlatt!
Introducing Equivariant Diffusion Policy, a novel sample efficient BC algorithm based on equivariant diffusion. Our method leverages the symmetry in policy denoising to boost learning — needing 5x less training data in sim and mastering complex tasks in real-world with <60 demos.
Perfect start to the #CoRL2024 week! Was a pleasure organizing the NextGen Robot Learning Symposium at @TUDarmstadt with @firasalhafez @GeorgiaChal Thanks to the speakers for the great talks! @YunzhuLiYZ @NimaFazeli7 @Dian_Wang_ @HaojieHuang13 @Vikashplus @ehsanik @Oliver_Kroemer
Excited to share our #CoRL2024 paper: OrbitGrasp—a fully SE(3)-equivariant grasp detection method for 6-DoF grasping! arxiv: arxiv.org/pdf/2407.03531
Grasp detection is crucial for robotic manipulation but remains challenging in SE(3). We introduce our #CoRL2024 paper: OrbitGrasp, an SE(3)-equivariant grasp learning framework using spherical harmonics for 6-DoF grasp detection. 🌐 orbitgrasp.github.io
Join us on Monday October. 14th at 2pm (UTC+4) in #IROS2024 Workshop on Equivariant Robotics. A great lineup of keynote speakers will discuss how symmetry penetrates each and every subfield of robotics. Website and Zoom Link: equirob2024.github.io
We have released the YouTube recording of our #RSS2024 workshop on "Geometric and Algebraic Structure in Robot Learning"! 🎥Youtube: youtube.com/playlist?list=… 🏠Webpage: sites.google.com/view/gas-rl-rs…
Instead of inferring a desired object pose directly, this method "imagines" a reconstruction of the entire scene in the target pose. Surprisingly, we find that this improves sample efficiency, even though we are inferring more information.
Checkout our new work, Imagination Policy. We leverage a point cloud diffusion model to “imagine” a target scene, then use SVD to calculate rigid transformations that bring objects to the imagined scene as robot actions. More importantly, Imagination Policy is bi-equivariant!
I'm excited about this work. The equivariant model significantly reduces the number of demos needed to train good diffusion policies.
Introducing Equivariant Diffusion Policy, a novel sample efficient BC algorithm based on equivariant diffusion. Our method leverages the symmetry in policy denoising to boost learning — needing 5x less training data in sim and mastering complex tasks in real-world with <60 demos.
Please check our latest work: Equivariant Diffusion Policy! This is fantastic work, which significantly improves the generalization ability, accuracy, and efficiency of diffusion policy!
Introducing Equivariant Diffusion Policy, a novel sample efficient BC algorithm based on equivariant diffusion. Our method leverages the symmetry in policy denoising to boost learning — needing 5x less training data in sim and mastering complex tasks in real-world with <60 demos.
Please consider submitting your paper and attending this interesting workshop!
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