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Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the policy to learn both task strategy and low-level motion generation. In this setting, the action representation is critical because it determines how policy outputs are converted into robot motion, shaping both exploration and physical execution. Direct Cartesian command interfaces require the policy to generate motion at every decision step, coupling task-level adaptation with continuous low-level control and increasing the learning burden. We pr

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