用自由能原理统一探索与鲁棒性，让机器人在真实环境可靠执行任务。
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
- 基于自由能原理，联合学习环境动态与奖励函数
- 在仿真和真实机械臂上实现零样本部署，减少模拟到现实的差距
- 适合需要高可靠性、无需任务调优的工业机器人应用
实现可靠机器人控制的关键挑战在于构建既能学习策略又能保障实际部署中鲁棒性的计算模型。受计算神经科学中自由能原理启发，我们提出一种策略计算模型，可联合学习环境动态与奖励函数，并确保对认知不确定性具有鲁棒性。通过扩展分布鲁棒性自由能原理，我们改进了最大扩散学习框架。显式刻画了策略在环境与奖励上的认知不确定性鲁棒性后，在连续控制基准测试中进行了验证，涵盖仿真与真实世界实验，使用Franka Research 3机械臂进行抓取操作。在仿真及零样本部署场景下，该方法有效缩小了模拟到现实的差距，实现了无需任务特定微调的可重复桌面操作。
原文摘要 · Abstract (English)
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the free energy principle in computational neuroscience, to address these challenges, we propose a model for policy computation that jointly learns environment dynamics and rewards, while ensuring robustness to epistemic uncertainties. Expounding a distributionally robust free energy principle, we propose a modification to the maximum diffusion learning framework. After explicitly characterizing robustness of our policies to epistemic uncertainties in both environment and reward, we validate their effectiveness on continuous-control benchmarks, via both simulations and real-world experiments involving manipulation with a Franka Research~3 arm. Across simulation and zero-shot deployment, our approach narrows the sim-to-real gap, and enables repeatable tabletop manipulation without task-specific fine-tuning.
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