Safety and optimality in learning-based control at low computational cost

• URL: http://arxiv.org/abs/2505.08026v1
• Date: Mon, 12 May 2025 19:50:47 GMT
• Title: Safety and optimality in learning-based control at low computational cost
• Authors: Dominik Baumann, Krzysztof Kowalczyk, Cristian R. Rojas, Koen Tiels, Pawel Wachel,
• Abstract summary: We propose CoLSafe, a lightweight safe learning algorithm for embedded devices.<n>We derive both safety and optimality guarantees and showcase the effectiveness of our algorithm on a seven-degrees-of-freedom robot arm.
• Score: 3.834396441954782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applying machine learning methods to physical systems that are supposed to act in the real world requires providing safety guarantees. However, methods that include such guarantees often come at a high computational cost, making them inapplicable to large datasets and embedded devices with low computational power. In this paper, we propose CoLSafe, a computationally lightweight safe learning algorithm whose computational complexity grows sublinearly with the number of data points. We derive both safety and optimality guarantees and showcase the effectiveness of our algorithm on a seven-degrees-of-freedom robot arm.

Related papers

• Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning [88.78080749909665]
  Current on-device training methods just focus on efficient training without considering the catastrophic forgetting.<n>This paper proposes a simple but effective edge-friendly incremental learning framework.<n>Our method achieves average accuracy boost of 38.08% with even less memory and approximate computation.
  arXiv  Detail & Related papers  (2024-06-13T05:49:29Z)
• A computationally lightweight safe learning algorithm [1.9295598343317182]
  We propose a safe learning algorithm that provides probabilistic safety guarantees but leverages the Nadaraya-Watson estimator. We provide theoretical guarantees for the estimates, embed them into a safe learning algorithm, and show numerical experiments on a simulated seven-degrees-of-freedom robot manipulator.
  arXiv  Detail & Related papers  (2023-09-07T12:21:22Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• 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)
• Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning [0.0]
  This work adopts control barrier functions over reinforcement learning, and proposes a compensated algorithm to completely maintain safety. Compared to quadratic programming based reinforcement learning methods, our sum-of-squares programming based reinforcement learning has shown its superiority.
  arXiv  Detail & Related papers  (2022-06-16T04:38:50Z)
• GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems [75.22958991597069]
  This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for high-dimensional systems. We demonstrate the superiority of GoSafeOpt over competing model-free safe learning methods on a robot arm.
  arXiv  Detail & Related papers  (2022-01-24T10:05:44Z)
• Safe Policy Optimization with Local Generalized Linear Function Approximations [17.84511819022308]
  Existing safe exploration methods guaranteed safety under the assumption of regularity. We propose a novel algorithm, SPO-LF, that optimize an agent's policy while learning the relation between a locally available feature obtained by sensors and environmental reward/safety. We experimentally show that our algorithm is 1) more efficient in terms of sample complexity and computational cost and 2) more applicable to large-scale problems than previous safe RL methods with theoretical guarantees.
  arXiv  Detail & Related papers  (2021-11-09T00:47:50Z)
• 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)
• Safeguarded Learned Convex Optimization [106.81731132086851]
  Analytic optimization algorithms can be hand-designed to provably solve problems in an iterative fashion. Data-driven algorithms can "learn to optimize" (L2O) with much fewer iterations and similar cost per iteration as general-purpose optimization algorithms. We present a Safe-L2O framework to fuse the advantages of these approaches.
  arXiv  Detail & Related papers  (2020-03-04T04:01:15Z)
• 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)

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.