Post-Convergence Sim-to-Real Policy Transfer: A Principled Alternative to Cherry-Picking

• URL: http://arxiv.org/abs/2504.15414v1
• Date: Mon, 21 Apr 2025 19:48:05 GMT
• Title: Post-Convergence Sim-to-Real Policy Transfer: A Principled Alternative to Cherry-Picking
• Authors: Dylan Khor, Bowen Weng,
• Abstract summary: This paper addresses the post-convergence sim-to-real transfer problem by introducing a worst-case performance transference optimization approach.<n>Experiments demonstrate its effectiveness in transferring RL-based locomotion policies from simulation to real-world laboratory tests.
• Score: 5.027571997864706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based approaches, particularly reinforcement learning (RL), have become widely used for developing control policies for autonomous agents, such as locomotion policies for legged robots. RL training typically maximizes a predefined reward (or minimizes a corresponding cost/loss) by iteratively optimizing policies within a simulator. Starting from a randomly initialized policy, the empirical expected reward follows a trajectory with an overall increasing trend. While some policies become temporarily stuck in local optima, a well-defined training process generally converges to a reward level with noisy oscillations. However, selecting a policy for real-world deployment is rarely an analytical decision (i.e., simply choosing the one with the highest reward) and is instead often performed through trial and error. To improve sim-to-real transfer, most research focuses on the pre-convergence stage, employing techniques such as domain randomization, multi-fidelity training, adversarial training, and architectural innovations. However, these methods do not eliminate the inevitable convergence trajectory and noisy oscillations of rewards, leading to heuristic policy selection or cherry-picking. This paper addresses the post-convergence sim-to-real transfer problem by introducing a worst-case performance transference optimization approach, formulated as a convex quadratic-constrained linear programming problem. Extensive experiments demonstrate its effectiveness in transferring RL-based locomotion policies from simulation to real-world laboratory tests.

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