Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
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.
