Article

Learning manipulation actions from demonstrations: online adaptation to latent physical properties

The authors propose a learning-from-demonstrations method that jointly learns a dynamics model and object-specific latent representations of physical properties, and conditions an imitation policy on online estimates of these representations, enabling the robot to adjust manipulation actions such as regrasping and contact locations based on physical-property information acquired through interaction. During execution, the dynamics model and policy weights remain fixed, and only the latent representation is updated based on the discrepancy between predicted and actual observations. In real-world and simulation experiments with objects having different centers of mass, online adaptation improved task success rates, but in simulation the success rate remained about 30% at center-of-mass positions not covered by the demonstrations, indicating limited adaptation to unseen physical properties.

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