Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model

• URL: http://arxiv.org/abs/2405.13735v2
• Date: Fri, 24 May 2024 19:29:48 GMT
• Title: Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model
• Authors: Alireza Nadali, Ashutosh Trivedi, Majid Zamani,
• Abstract summary: Control barrier certificates have proven effective in formally guaranteeing the safety of the control systems. Design of a control barrier certificate is a time-consuming and computationally expensive endeavor. We propose a validity condition that, when met, guarantees correctness of the controller.
• Score: 4.7962647777554634
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Control barrier certificates have proven effective in formally guaranteeing the safety of the control systems. However, designing a control barrier certificate is a time-consuming and computationally expensive endeavor that requires expert input in the form of domain knowledge and mathematical maturity. Additionally, when a system undergoes slight changes, the new controller and its correctness certificate need to be recomputed, incurring similar computational challenges as those faced during the design of the original controller. Prior approaches have utilized transfer learning to transfer safety guarantees in the form of a barrier certificate while maintaining the control invariant. Unfortunately, in practical settings, the source and the target environments often deviate substantially in their control inputs, rendering the aforementioned approach impractical. To address this challenge, we propose integrating \emph{inverse dynamics} -- a neural network that suggests required action given a desired successor state -- of the target system with the barrier certificate of the source system to provide formal proof of safety. In addition, we propose a validity condition that, when met, guarantees correctness of the controller. We demonstrate the effectiveness of our approach through three case studies.

Related papers

• Formal Verification of Neural Certificates Done Dynamically [7.146556437126553]
  We propose a lightweight runtime monitoring framework that integrates real-time verification and does not require access to the underlying control policy.<n>Our approach enables timely detection of safety violations and incorrect certificates with minimal overhead.
  arXiv  Detail & Related papers  (2025-07-16T07:37:23Z)
• Safely Learning Controlled Stochastic Dynamics [61.82896036131116]
  We introduce a method that ensures safe exploration and efficient estimation of system dynamics.<n>After training, the learned model enables predictions of the system's dynamics and permits safety verification of any given control.<n>We provide theoretical guarantees for safety and derive adaptive learning rates that improve with increasing Sobolev regularity of the true dynamics.
  arXiv  Detail & Related papers  (2025-06-03T11:17:07Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates [45.72598064481916]
  This paper considers a learning-based setting with a robust safety certificate based on a control barrier function (CBF) second-order cone program. If the control barrier function certificate is feasible, our approach leverages it to guarantee safety. Otherwise, our method explores the system dynamics to collect data and recover the feasibility of the control barrier function constraint.
  arXiv  Detail & Related papers  (2023-11-03T14:23:57Z)
• A General Framework for Verification and Control of Dynamical Models via Certificate Synthesis [54.959571890098786]
  We provide a framework to encode system specifications and define corresponding certificates. We present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks.
  arXiv  Detail & Related papers  (2023-09-12T09:37:26Z)
• 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)
• Safe Reinforcement Learning via Confidence-Based Filters [78.39359694273575]
  We develop a control-theoretic approach for certifying state safety constraints for nominal policies learned via standard reinforcement learning techniques. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-07-04T11:43:23Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning [0.0]
  We propose an automated verification pipeline capable of synthesizing high-quality safety shields. Our key insight involves separating safety verification from neural controller, using pre-computed verified safety shields to constrain neural controller training. Experimental results over a range of realistic high-dimensional deep RL benchmarks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-04-20T19:30:29Z)
• Runtime Safety Assurance Using Reinforcement Learning [37.61747231296097]
  This paper aims to design a meta-controller capable of identifying unsafe situations with high accuracy. We frame the design of RTSA with the Markov decision process (MDP) and use reinforcement learning (RL) to solve it.
  arXiv  Detail & Related papers  (2020-10-20T20:54:46Z)
• Runtime-Safety-Guided Policy Repair [13.038017178545728]
  We study the problem of policy repair for learning-based control policies in safety-critical settings. We propose to reduce or even eliminate control switching by repairing' the trained policy based on runtime data produced by the safety controller.
  arXiv  Detail & Related papers  (2020-08-17T23:31:48Z)

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.