风险感知域随机化可重塑接触密集型控制的优化景观。
On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
- 引入风险感知域随机化，调整采样优化器的代价景观。
- 悲观策略在接触动作附近形成更稳定的吸引盆。
- 适合研究模型不确定性下的接触密集型机器人控制。
域随机化（DR）广泛用于策略学习以提升对建模误差的鲁棒性，但在接触密集型采样式预测控制（SPC）中仍研究不足，因滚动预测质量对不确定性高度敏感。本文首次在典型的Push-T任务上研究风险感知的域随机化，比较平均、乐观和悲观的滚动聚合方式在随机模型实例下的表现。初步结果表明，DR不仅影响对建模误差的鲁棒性，还通过重塑接触产生动作附近的吸引盆，改变采样优化器所见的有效代价景观。这为在模型不确定性下探索更稳健的风险感知接触密集型SPC提供了可能。视频：https://youtu.be/f1F0ALXxhSM
原文摘要 · Abstract (English)
Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-based predictive control (SPC), where rollout quality is highly sensitive to uncertainty. In this work, we take the first step by studying risk-aware DR in predictive sampling on a simple yet representative Push-T task, comparing average, optimistic, and pessimistic rollout aggregations under randomized model instances. Our initial results suggest that DR affects not only robustness to model error, but also the effective cost landscape seen by the sampling-based optimizer, by reshaping the basin of attraction around contact-producing actions. This opens up potential for exploring better grounded risk-aware contact-rich SPC under model uncertainty. Video: https://youtu.be/f1F0ALXxhSM
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