为机器人策略的仿真到现实迁移设计评估基准
Robot Policy Evaluation for Sim-to-Real Transfer: A Benchmarking Perspective
- 用高视觉保真度仿真提升仿真到现实的迁移效果
- 通过逐步增加任务复杂度和场景扰动测试策略鲁棒性
- 量化真实世界与仿真中性能的一致性，适合做迁移研究者
当前基于视觉的机器人仿真基准已显著推动机器人操作研究。然而，机器人本质上是现实世界问题，针对通用策略的现实应用评估仍滞后于仿真评估。本文探讨了为实现仿真到现实策略迁移而设计通用机器人操作策略评估基准所面临的挑战与理想要求。提出三点建议：1）利用高视觉保真度仿真以提升仿真到现实的迁移能力；2）通过系统性地增加任务复杂度与场景扰动来评估策略的鲁棒性；3）量化真实世界表现与其仿真对应物之间的性能一致性。
原文摘要 · Abstract (English)
Current vision-based robotics simulation benchmarks have significantly advanced robotic manipulation research. However, robotics is fundamentally a real-world problem, and evaluation for real-world applications has lagged behind in evaluating generalist policies. In this paper, we discuss challenges and desiderata in designing benchmarks for generalist robotic manipulation policies for the goal of sim-to-real policy transfer. We propose 1) utilizing high visual-fidelity simulation for improved sim-to-real transfer, 2) evaluating policies by systematically increasing task complexity and scenario perturbation to assess robustness, and 3) quantifying performance alignment between real-world performance and its simulation counterparts.
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