Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy

2026-03-06

AI Summary

This work addresses the challenge of achieving robotic control that is both adaptive and robust in real-world environments characterized by environmental dynamics and epistemic uncertainty in reward signals. The authors propose the Distributionally Robust Free Energy Principle, which formalizes policy robustness under cognitive uncertainty and integrates it into a maximum entropy reinforcement learning framework, thereby unifying exploration and robustness. The approach requires no task-specific fine-tuning and enables zero-shot deployment. Combined with simulation-to-reality transfer techniques, the method achieves reproducible tabletop manipulation tasks on a Franka Emika robotic arm, substantially narrowing the sim-to-real gap and demonstrating strong effectiveness and generalization in continuous control settings.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• robust control
• epistemic uncertainty
• distributional robustness
• reliable robotics
• sim-to-real gap

Innovation

• distributional robustness
• free energy principle
• epistemic uncertainty
• sim-to-real transfer
• maximum diffusion learning