Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations

• URL: http://arxiv.org/abs/2011.04141v1
• Date: Mon, 9 Nov 2020 01:59:14 GMT
• Title: Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
• Authors: Glen Chou, Necmiye Ozay, Dmitry Berenson
• Abstract summary: 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. We derive guarantees on the accuracy of our constraint belief and probabilistic guarantees on plan safety.
• Score: 6.950510860295866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SafeDiffuser: Safe Planning with Diffusion Probabilistic Models [97.80042457099718]
  Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees. We propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation.
  arXiv  Detail & Related papers  (2023-05-31T19:38:12Z)
• Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems [1.0323063834827415]
  We propose a sampling-based planner that uses the mean dynamics model and simultaneously bounds the closed-loop tracking error via a learned disturbance bound. We validate that our guarantees translate to empirical safety in simulation on a 10D quadrotor, and in the real world on a physical CrazyFlie quadrotor and Clearpath Jackal robot.
  arXiv  Detail & Related papers  (2022-12-13T19:38:39Z)
• Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes [9.861651769846578]
  We develop a safety shield for nonlinear continuous systems that solve reach-avoid tasks. Our approach is called action projection and is implemented via mixed-integer optimization. In contrast to other state of the art approaches for action projection, our safety shield can efficiently handle input constraints and obstacles.
  arXiv  Detail & Related papers  (2022-10-19T16:06:12Z)
• 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)
• ProBF: Learning Probabilistic Safety Certificates with Barrier Functions [31.203344483485843]
  The control barrier function is a useful tool to guarantee safety if we have access to the ground-truth system dynamics. In practice, we have inaccurate knowledge of the system dynamics, which can lead to unsafe behaviors. We show the efficacy of this method through experiments on Segway and Quadrotor simulations.
  arXiv  Detail & Related papers  (2021-12-22T20:18:18Z)
• Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants [7.216586291939535]
  We present a method for feedback motion planning of systems with unknown dynamics. We provide guarantees on safety, reachability, and goal stability. We demonstrate our approach by planning using learned models of a 6D quadrotor and a 7DOF Kuka arm.
  arXiv  Detail & Related papers  (2020-10-18T14:17:53Z)
• 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)
• Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
  We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs) We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system. To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
  arXiv  Detail & Related papers  (2020-04-07T12:29:06Z)
• Cautious Reinforcement Learning with Logical Constraints [78.96597639789279]
  An adaptive safe padding forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process. Theoretical guarantees are available on the optimality of the synthesised policies and on the convergence of the learning algorithm.
  arXiv  Detail & Related papers  (2020-02-26T00:01:08Z)
• Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty [6.950510860295866]
  We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner. Our method uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations within a mixed integer linear program (MILP) to learn constraints which are consistent with the local optimality of the demonstrations. We evaluate our method on high-dimensional constraints and systems by learning constraints for 7-DOF arm and quadrotor examples, show that it outperforms competing constraint-learning approaches, and can be effectively used to plan new constraint-satisfying trajectories in the environment
  arXiv  Detail & Related papers  (2020-01-25T15:57:48Z)

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.