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Diffusion Policy
A visuomotor policy approach using diffusion to generate robot actions.
Robot policiesTHE EMBODIED AI DIRECTORY
Find imitation-learning and learned-control policies for robotic tasks.
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A visuomotor policy approach using diffusion to generate robot actions.
Robot policies
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A generalist robot policy framework for adapting across manipulation tasks.
Robot policies
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A policy-learning implementation for bimanual robot manipulation.
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CLOVER: Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation
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Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation
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A language-conditioned framework for visual pick-and-place policies.
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HoloMotion provides a foundation-model project for whole-body humanoid control.
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Code accompanying the paper "Learning Agile Robotic Locomotion Skills by Imitating Animals"
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Robot manipulation model resources from the Allen Institute for AI.
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Official codebase for Manipulation Primitive-augmented reinforcement Learning (MAPLE)
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DexMV: Imitation Learning for Dexterous Manipulation from Human Videos, ECCV 2022
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Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies
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Official repository of T-Rex: Tactile-Reactive Dexterous Manipulation
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Language-guided whole-body control for general humanoid robots
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"FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation"
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Official implementation of the paper: "PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System"
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Natural Locomotion, Jumping and Recovery of Quadruped Robot A1 with AMP
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Codebase for DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
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A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
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Visual Imitation Enables Contextual Humanoid Control. CoRL 2025, Best Student Paper Award.
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Being-H is BeingBeyond's family of human-centric embodied foundation models.
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https://arxiv.org/abs/2312.10807
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3D-CovDiffusion implements a 3D-aware diffusion policy for coverage path planning.
Robot policies
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Official Code for "Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning"
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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