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RoboOmni · OpenMOSS
A proactive robot manipulation model for multimodal physical environments
Robot policiesTHE EMBODIED AI DIRECTORY
Find imitation-learning and learned-control policies for robotic tasks.
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A proactive robot manipulation model for multimodal physical environments
Robot policies
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We present the first successful deployment of a whole-body sampling-based MPC system on a real-world quadruped robot. Our approach enables real-time, contact-rich locomotion and manipulation using…
Robot policies
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RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance
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This is code repo for the paper, Depolying Ten Thousand Robots: Scalable Imitation Learninig for Lifelong Mulit-Agent Path Finding, which won the ICRA 2025 best paper on multi-robot systems and…
Robot policies
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The official implementation of the paper "Scaling Behavior Foundation Model for Humanoid Robots"
Robot policies
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"Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation" code repository
Robot policies
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Official implementation of SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
Robot policies
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Official codebase for Sirius: Robot Learning on the Job
Robot policies
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Official Implementation of the Universal Humanoid Controller in Mujoco. Supports Kinpoly (NeurIPS 2021) and EmbodiedPose (NeurIPS 2022).
Robot policies
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ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
Robot policies
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Repository for our paper: Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations Proceedings of the 6th Conference on Robot Learning (CoRL),
Robot policies
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Learning Whole-Body Dexterous Loco-Manipulation from Human–Object Interactions
Robot policies
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XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
Robot policies
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Code for my paper "You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations"
Robot policiesSELECTION NOTES
Check how actions are represented and what demonstrations or rewards were used. Assess distribution shift and closed-loop behavior before using a trained policy on a different robot.
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