Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
This addresses the challenge of ensuring safety in robotic motion planning under uncertainty, which is incremental as it builds on existing constraint learning and robust optimization methods.
The paper tackles the problem of learning uncertain constraints from demonstrations for safe motion planning, achieving probabilistic safety guarantees and outperforming baselines in safety and efficiency on a 7-DOF arm and 12D quadrotor with up to 30D constraints.
We present a method for learning to satisfy uncertain constraints from demonstrations. Our method uses robust optimization to obtain a belief over the potentially infinite set of possible constraints consistent with the demonstrations, and then uses this belief to plan trajectories that trade off performance with satisfying the possible constraints. We use these trajectories in a closed-loop policy that executes and replans using belief updates, which incorporate data gathered during execution. We derive guarantees on the accuracy of our constraint belief and probabilistic guarantees on plan safety. We present results on a 7-DOF arm and 12D quadrotor, showing our method can learn to satisfy high-dimensional (up to 30D) uncertain constraints, and outperforms baselines in safety and efficiency.