Online Adaptation to Latent Physical Properties for Learning from Demonstration

1. 05:00
   arXiv Robotics — research abstractsSignal

   Online Adaptation to Latent Physical Properties for Learning from Demonstration

   This paper presents a learning-from-demonstration approach that adapts manipulation actions by learning latent representations of physical properties. The method jointly trains a dynamics model and object-specific latent variables from demonstrations, using these to condition an imitation policy. During execution, the model and policy weights are fixed while latent representations are updated based on discrepancies between predicted and actual observations. Experiments with objects having different centers of mass show improved task success rates through online adaptation, but simulation results indicate limited generalization to unseen physical properties, with success rates around 30% for center-of-mass locations not present in the training demonstrations.

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