Helping Hands Lab @ Northeastern
@HelpingHandsLab
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🤖 Robotic Manipulation | Reinforcement Learning | PI Rob Platt @RobotPlatt @KhouryCollege @Northeastern
Boston, Massachusetts
Joined March 2022
#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.
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RT @Dian_Wang_: Honored to receive my first best paper nomination from @corl_conf! Had such a great time at #CoRL2024, huge thanks to the o….
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RT @SnehalJauhri: Perfect start to the #CoRL2024 week!.Was a pleasure organizing the NextGen Robot Learning Symposium at @TUDarmstadt with….
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Paper link: Website: Coauthors: @Dian_Wang_, @BizaOndrej, @RubyFreax, @SeanLiu7081, @SaulBadman12, @RobotPlatt, @RobinSFWalters.
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#CoRL2024 IMAGINATION POLICY: Using Generative Point Cloud Models for Learning Manipulation Policies Led by @HaojieHuang13. A key-frame multi-task policy can generate key poses (imagine) and do manipulation precisely with sample efficiency. Presenting at Poster Session 4.
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Paper link: Website: Coauthors: @XupengZ, @BizaOndrej, Shuo Jiang, @LinfengZhaoZLF, @HaojieHuang13, @yuqi_Beijing, @RobotPlatt.
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#CoRL2024 ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter by @RubyFreax. A plug-and-play vision-language grasping system that uses GPT-4o’s advanced contextual reasoning for heavy clutter environment grasping strategies. Presenting at Poster Session 3.
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Paper link: Website: Coauthors: Stephen Hart, David Surovik, @tarikkelestemur, @HaojieHuang13, @ZhaoHaibo47588, Mark Yeatman, Jiuguang Wang, @RobinSFWalters, @RobotPlatt.
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Presenting at Poster Session 2. Paper link: Coauthors: Long Dinh Van The @cjdamato @RobotPlatt.
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#CoRL2024 Leveraging Mutual Information for Asymmetric Learning under Partial Observability led by @HaiNguy69482974.Addressing asymmetric learning under partial observability (state availability at training) by rewarding actions leads to histories that gain info about the state.
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Paper link: Website: Coauthors: @XupengZ @Dian_Wang_ Zihao Dong @HaojieHuang13 @330781570 @RobinSFWalters @RobotPlatt.
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Excited to share our #CoRL2024 paper: OrbitGrasp—a fully SE(3)-equivariant grasp detection method for 6-DoF grasping! .arxiv:
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. 🌐
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RT @EChatzipantazis: Join us on Monday October. 14th at 2pm (UTC+4) in #IROS2024 Workshop on Equivariant Robotics. A great lineup of keyno….
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RT @LinfengZhaoZLF: We have released the YouTube recording of our #RSS2024 workshop on "Geometric and Algebraic Structure in Robot Learning….
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RT @RobotPlatt: Instead of inferring a desired object pose directly, this method "imagines" a reconstruction of the entire scene in the tar….
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RT @RobotPlatt: I'm excited about this work. The equivariant model significantly reduces the number of demos needed to train good diffusion….
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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.
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Please consider submitting your paper and attending this interesting workshop!.
🤖 Excited for #RSS2024? Don’t miss our workshop on "Geometric and Algebraic Structure in Robot Learning"! Submit workshop papers (by 6/10 AOE) and dive into discussions on leveraging these structures for enhanced #robotics. 🚀🔍 Join us on 07/19 in Delft, Netherlands!
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How can we combine the benefits of Fourier Transformation to further facilitate the efficiency of policy learning? In our Fourier Transporter (FouTran), we introduce a novel 3D Pick-Place Model with Bi-equivariance property. Paper:
#ICLR24.We proposed FourTran, a very sample-efficient 3D manipulation pick-place model. 1. It can learn a nontrivial 3D policy with less than 10 demos. 2. It represents 3D action distribution in Fourier Space. Check it in the Poster Session 4 at 4:30 PM Vienna time (10:30 EDT)
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