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Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations

Liqun Zhao, Keyan Miao, Konstantinos Gatsis, Antonis Papachristodoulou

TL;DR

This work tackles safety and stability in human-aligned reinforcement learning by learning a NODE-based model of human and robot dynamics and enforcing safety and stability through discrete-time CBFs and CLFs integrated with SAC. It introduces an augmented Lagrangian framework to train the primary controller under CBF/CLF constraints and a Lyapunov-based stability objective, plus a safety-first backup controller to handle constraint conflicts. The framework is validated in a Simulated Car Following scenario, where the NODE-based approach yields higher cumulative rewards and fewer safety violations than baselines, demonstrating improved sample efficiency without requiring a nominal model. Overall, the method advances practical safe-and-stable RL for human-robot interaction, while acknowledging potential dynamics-model gaps and suggesting directions for handling more complex human-driven environments.

Abstract

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.

Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations

TL;DR

This work tackles safety and stability in human-aligned reinforcement learning by learning a NODE-based model of human and robot dynamics and enforcing safety and stability through discrete-time CBFs and CLFs integrated with SAC. It introduces an augmented Lagrangian framework to train the primary controller under CBF/CLF constraints and a Lyapunov-based stability objective, plus a safety-first backup controller to handle constraint conflicts. The framework is validated in a Simulated Car Following scenario, where the NODE-based approach yields higher cumulative rewards and fewer safety violations than baselines, demonstrating improved sample efficiency without requiring a nominal model. Overall, the method advances practical safe-and-stable RL for human-robot interaction, while acknowledging potential dynamics-model gaps and suggesting directions for handling more complex human-driven environments.

Abstract

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.
Paper Structure (13 sections, 15 equations, 1 figure, 2 algorithms)

This paper contains 13 sections, 15 equations, 1 figure, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Preliminaries
  4. Markov Decision Process
  5. Definitions of Safety and Stability
  6. Introduction to Neural Ordinary Equations (NODEs)
  7. Definition of the Safe and Stable Control Problem
  8. Framework Design
  9. Learning the Dynamics via NODEs and Value Function of the Cost
  10. Augmented Lagrangian Method for Parameter Updating
  11. Backup Controller Design
  12. Simulations
  13. CONCLUSIONS

Figures (1)

  • Figure 1: The cumulative reward and cumulative number of safety violations of each episode in the simulated car following environment setting are compared between the proposed method (drawn in blue) and baselines. Each curve illustrates the average across ten experiments employing different random seeds, with the shaded area denoting the standard deviation. The safety violation of the SAC-RCBF algorithm keeps being 0 and therefore its graph is coincident with the X-axis.

Theorems & Definitions (2)

  • Definition 1: Discrete-time Control Barrier Function xiong2022discrete
  • Definition 2: Stability in Mean Cost