CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems

• URL: http://arxiv.org/abs/2307.08602v2
• Date: Sun, 13 Aug 2023 20:36:46 GMT
• Title: CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
• Authors: Hiroyasu Tsukamoto and Benjamin Rivi\`ere and Changrak Choi and Amir Rahmani and Soon-Jo Chung
• Abstract summary: CaRT is a new hierarchical, distributed architecture to guarantee the safety and robustness of a learning-based motion planning policy. We show that CaRT guarantees safety and the exponentialness of the trajectory tracking error, even under the presence of deterministic and bounded disturbance. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.
• Score: 7.77024796789203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-based motion planning policy. First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy. Second, in off-nominal settings, the analytical form of our CaRT robust filter optimally tracks the certified safe trajectory, generated by the previous layer in the hierarchy, the CaRT safety filter. We show using contraction theory that CaRT guarantees safety and the exponential boundedness of the trajectory tracking error, even under the presence of deterministic and stochastic disturbance. Also, the hierarchical nature of CaRT enables enhancing its robustness for safety just by its superior tracking to the certified safe trajectory, thereby making it suitable for off-nominal scenarios with large disturbances. This is a major distinction from conventional safety function-driven approaches, where the robustness originates from the stability of a safe set, which could pull the system over-conservatively to the interior of the safe set. Our log-barrier formulation in CaRT allows for its distributed implementation in multi-agent settings. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.

Related papers

• ConstrainedZero: Chance-Constrained POMDP Planning using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints [34.9739641898452]
  This work introduces the ConstrainedZero policy algorithm that solves CC-POMDPs in belief space by learning neural network approximations of the optimal value and policy. Results show that by separating safety constraints from the objective we can achieve a target level of safety without optimizing the balance between rewards and costs.
  arXiv  Detail & Related papers  (2024-05-01T17:17:22Z)
• Learning Predictive Safety Filter via Decomposition of Robust Invariant Set [6.94348936509225]
  This paper presents advantages of both RMPC and RL RL to synthesize safety filters for nonlinear systems. We propose a policy approach for robust reach problems and establish its complexity.
  arXiv  Detail & Related papers  (2023-11-12T08:11:28Z)
• Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon [7.55113002732746]
  Safety, the satisfaction of state and input constraints, can be guaranteed by augmenting the learned control policy with a safety filter. Model predictive safety filters (MPSFs) are a common safety filtering approach based on model predictive control (MPC)
  arXiv  Detail & Related papers  (2023-09-20T16:35:29Z)
• ISAACS: Iterative Soft Adversarial Actor-Critic for Safety [0.9217021281095907]
  This work introduces a novel approach enabling scalable synthesis of robust safety-preserving controllers for robotic systems. A safety-seeking fallback policy is co-trained with an adversarial "disturbance" agent that aims to invoke the worst-case realization of model error. While the learned control policy does not intrinsically guarantee safety, it is used to construct a real-time safety filter.
  arXiv  Detail & Related papers  (2022-12-06T18:53:34Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.