Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation

Abstract:This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque and angular displacement, and train a differentiable energy predictor that estimates this work from robot states and actions. The predictor converts a non-differentiable simulator-side physical quantity into a differentiable regularizer for fine-tuning a pretrained manipulation policy. We instantiate the framework with RVT-2 on RLBench and evaluate 12 manipulation tasks involving object contact, articulated motion, placement, pushing, and sweeping. The proposed fine-tuning reduces the average mechanical work from 208.8J to 204.4J (i.e., 2.1% reduction), while the mean task success rate also increases slightly from 86.2% to 86.9%. These results show that work-aware policy optimization can suppress physically inefficient motion without requiring an explicit differentiable dynamics model.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.18164 [cs.RO]

(or arXiv:2609.18164v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.18164 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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