Learning Provably Robust Motion Planners Using Funnel Libraries
This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set. We achieve this by bringing together tools from generalization theory and robust control. First, we curate a library of motion primitives where the robustness of each primitive is characterized by an over-approximation of the forward reachable set, i.e., a "funnel". Then, we optimize probably approximately correct (PAC)-Bayes generalization bounds for training our planner to compose these primitives such that the entire funnels respect the problem specification. We demonstrate the ability of our approach to provide strong guarantees on two simulated examples: (i) navigation of an autonomous vehicle under external disturbances on a five-lane highway with multiple vehicles, and (ii) navigation of a drone across an obstacle field in the presence of wind disturbances.
Code (0)
등록된 구현이 없습니다.
Tasks
Generalization BoundsSimilar Papers 제목 키워드 기반
Funnel Libraries for Real-Time Robust Feedback Motion Planning
We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios whe…
Motion PlanningGNN-DIP: Neural Corridor Selection for Decomposition-Based Motion Planning
Motion planning through narrow passages remains a core challenge: sampling-based planners rarely place samples inside these narrow but critical regions, and even when samples land inside a passage, the straight-line conn…
Graph Neural NetworkMotion PlanningSmooth Feedback Motion Planning with Reduced Curvature
Feedback motion planning over cell decompositions provides a robust method for generating collision-free robot motion with formal guarantees. However, existing algorithms often produce paths with unnecessary bending, lea…
Motion PlanningSafe Gap-based Planning in Dynamic Settings
This chapter extends the family of perception-informed gap-based local planners to dynamic environments. Existing perception-informed local planners that operate in dynamic environments often rely on emergent or empirica…
Collision AvoidanceProbably Approximately Correct Vision-Based Planning using Motion Primitives
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes fr…
Reinforcement Learning