能動探索を用いたサンプリングベースシステム同定による脚ロボットのSim2Real学習
Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning
脚ロボットの物理パラメータを並列サンプリングで同定し、フィッシャー情報量を最大化する能動探索で実世界データを収集することで、シミュレーションと実機のギャップを縮め、移動タスクの性能を42〜63%向上させた。
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著者: Nikhil Sobanbabu, Guanqi He, Tairan He, Yuxiang Yang, Guanya Shi
分類: cs.RO
原文アブストラクト
Sim-to-real discrepancies hinder learning-based policies from achieving high-precision tasks in the real world. While Domain Randomization (DR) is commonly used to bridge this gap, it often relies on heuristics and can lead to overly conservative policies with degrading performance when not properly tuned. System Identification (Sys-ID) offers a targeted approach, but standard techniques rely on differentiable dynamics and/or direct torque measurement, assumptions that rarely hold for contact-rich legged systems. To this end, we present SPI-Active (Sampling-based Parameter Identification with Active Exploration), a two-stage framework that estimates physical parameters of legged robots to minimize the sim-to-real gap. SPI-Active robustly identifies key physical parameters through massive parallel sampling, minimizing state prediction errors between simulated and real-world trajectories. To further improve the informativeness of collected data, we introduce an active exploration strategy that maximizes the Fisher Information of the collected real-world trajectories via optimizing the input commands of an exploration policy. This targeted exploration leads to accurate identification and better generalization across diverse tasks. Experiments demonstrate that SPI-Active enables precise sim-to-real transfer of learned policies to the real world, outperforming baselines by 42-63% in various locomotion tasks.
