Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe

• URL: http://arxiv.org/abs/2106.11881v1
• Date: Tue, 22 Jun 2021 15:51:52 GMT
• Title: Failing with Grace: Learning Neural Network Controllers that are Boundedly Unsafe
• Authors: Panagiotis Vlantis and Michael M. Zavlanos
• Abstract summary: We consider the problem of learning a feed-forward neural network (NN) controller to safely steer an arbitrarily shaped robot in a compact workspace. We propose an approach that lifts such assumptions on the data that are hard to satisfy in practice. We provide a simulation study that verifies the efficacy of the proposed scheme.
• Score: 18.34490939288318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we consider the problem of learning a feed-forward neural network (NN) controller to safely steer an arbitrarily shaped planar robot in a compact and obstacle-occluded workspace. Unlike existing methods that depend strongly on the density of data points close to the boundary of the safe state space to train NN controllers with closed-loop safety guarantees, we propose an approach that lifts such assumptions on the data that are hard to satisfy in practice and instead allows for graceful safety violations, i.e., of a bounded magnitude that can be spatially controlled. To do so, we employ reachability analysis methods to encapsulate safety constraints in the training process. Specifically, to obtain a computationally efficient over-approximation of the forward reachable set of the closed-loop system, we partition the robot's state space into cells and adaptively subdivide the cells that contain states which may escape the safe set under the trained control law. To do so, we first design appropriate under- and over-approximations of the robot's footprint to adaptively subdivide the configuration space into cells. Then, using the overlap between each cell's forward reachable set and the set of infeasible robot configurations as a measure for safety violations, we introduce penalty terms into the loss function that penalize this overlap in the training process. As a result, our method can learn a safe vector field for the closed-loop system and, at the same time, provide numerical worst-case bounds on safety violation over the whole configuration space, defined by the overlap between the over-approximation of the forward reachable set of the closed-loop system and the set of unsafe states. Moreover, it can control the tradeoff between computational complexity and tightness of these bounds. Finally, we provide a simulation study that verifies the efficacy of the proposed scheme.

Related papers

• Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints [50.920465513162334]
  We introduce the PCBF algorithm to maximize the inner safe set of dynamical systems under input constraints. We validate its effectiveness through comparison with Hamilton-Jacobi reachability for an inverted pendulum and through simulations on a 12-dimensional quadrotor system. Results show that the PCBF consistently outperforms existing methods, yielding larger safe sets and ensuring safety under input constraints.
  arXiv  Detail & Related papers  (2024-10-05T18:45:19Z)
• 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)
• 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)
• BarrierNet: A Safety-Guaranteed Layer for Neural Networks [50.86816322277293]
  BarrierNet allows the safety constraints of a neural controller be adaptable to changing environments. We evaluate them on a series of control problems such as traffic merging and robot navigations in 2D and 3D space.
  arXiv  Detail & Related papers  (2021-11-22T15:38:11Z)
• Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian [5.686699342802045]
  We propose a separated proportional-integral Lagrangian algorithm to enhance RL safety under uncertainty. We demonstrate our method can reduce the oscillations and conservatism of RL policy in a car-following simulation.
  arXiv  Detail & Related papers  (2021-08-26T07:34:14Z)
• Constrained Feedforward Neural Network Training via Reachability Analysis [0.0]
  It remains an open challenge to train a neural network to obey safety constraints. This work proposes a constrained method to simultaneously train and verify a feedforward neural network with rectified linear unit (ReLU) nonlinearities.
  arXiv  Detail & Related papers  (2021-07-16T04:03:01Z)
• Safe Learning of Uncertain Environments for Nonlinear Control-Affine Systems [10.918870296899245]
  We consider the problem of safe learning in nonlinear control-affine systems subject to unknown additive uncertainty. We model uncertainty as a Gaussian signal and use state measurements to learn its mean and covariance bounds. We show that with an arbitrarily large probability we can guarantee that the state will remain in the safe set, while learning and control are carried out simultaneously.
  arXiv  Detail & Related papers  (2021-03-02T01:58:02Z)
• 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)
• Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach [2.741266294612776]
  We propose a model-free safety specification method that learns the maximal probability of safe operation. Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage. It yields a sequence of safe policies that determine the range of safe operation, called the safe set.
  arXiv  Detail & Related papers  (2020-02-24T09:20:03Z)

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.