バリア証明を用いた半教師あり安全視覚運動ポリシー合成
Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier Certificates
視覚運動データから、完全な安全ラベルなしでバリア証明を活用し、形式的な安全性保証を備えた制御ポリシーを半教師あり学習で合成する手法を提案。
著者: Manan Tayal, Aditya Singh, Pushpak Jagtap, Shishir Kolathaya
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In modern robotics, addressing the lack of accurate state space information in real-world scenarios has led to a significant focus on utilizing visuomotor observation to provide safety assurances. Although supervised learning methods, such as imitation learning, have demonstrated potential in synthesizing control policies based on visuomotor observations, they require ground truth safety labels for the complete dataset and do not provide formal safety assurances. On the other hand, traditional control-theoretic methods like Control Barrier Functions (CBFs) and Hamilton-Jacobi (HJ) Reachability provide formal safety guarantees but depend on accurate knowledge of system dynamics, which is often unavailable for high-dimensional visuomotor data. To overcome these limitations, we propose a novel approach to synthesize a semi-supervised safe visuomotor policy using barrier certificates that integrate the strengths of model-free supervised learning and model-based control methods. This framework synthesizes a provably safe controller without requiring safety labels for the complete dataset and ensures completeness guarantees for both the barrier certificate and the policy. We validate our approach through distinct case studies: an inverted pendulum system and the obstacle avoidance of an autonomous mobile robot.
