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Learning Robot Manipulation from Cross-Morphology Demonstration
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abstract
Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the case where the teacher's morphology is substantially different from that of the student. Our framework, Morphological Adaptation in Imitation Learning (MAIL), bridges this gap allowing us to train an agent from demonstrations by other agents with significantly different morphologies. MAIL learns from suboptimal demonstrations, so long as they provide $\textit{some}$ guidance towards a desired solution. We demonstrate MAIL on manipulation tasks with rigid and deformable objects including 3D cloth manipulation interacting with rigid obstacles. We train a visual control policy for a robot with one end-effector using demonstrations from a simulated agent with two end-effectors. MAIL shows up to $24\%$ improvement in a normalized performance metric over LfD and non-LfD baselines. It is deployed to a real Franka Panda robot, handles multiple variations in properties for objects (size, rotation, translation), and cloth-specific properties (color, thickness, size, material). An overview is on https://uscresl.github.io/mail .
Forward citations
Cited by 3 Pith papers
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Learning Efficient Robotic Garment Manipulation with Standardization
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Reinforcement Learning from Wild Animal Videos
A quadruped robot acquires walking, jumping, running-like, and standing skills using only the output of a video classifier trained on wild-animal videos as its reinforcement learning reward.
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UniLegs: Universal Multi-Legged Robot Control through Morphology-Agnostic Policy Distillation
A two-stage teacher-student distillation produces a single Transformer policy that reaches 94.47% of specialist teacher reward on five training morphologies and 72.64% on an unseen quadruped.
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