Artificial Intelligence - A Formal Methods Approach to Interpretable Reinforcement Learning for Robotic Planning

Oct 23, 2026

Upload by jay

Reinforcement learning for robotic planning and control is increasingly used, but learned policies raise predictability and safety concerns, while reward functions for complex tasks are difficult to define correctly and may be exploited. A formal methods approach is proposed that specifies rich task requirements, clarifies reward generation for interpretation, guides policy synthesis using the specification, and ensures the safety component holds. The framework combines a task-tailored predicate temporal logic with an automaton-guided safe RL algorithm using control barrier functions, illustrated on a robotic hot-dog cooking task.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Formal specification for interpretable rewards

Safety guarantees with control barrier functions

Related work

Value alignment and policy verification

ARTIFICIAL INTELLIGENCE A formal methods approach to interpretable reinforcement learning for robotic planning Xiao Li1*, Zachary Serlin1, Guang Yang2, Calin Belta1,2 Growing interest in reinforcement learning approaches to robotic planning and control raises concerns of predictability and safety of robot behaviors realized solely through learned control policies. In addition, formally defining reward functions for complex tasks is challenging, and faulty rewards are prone to exploitation by the learning agent. Here, we propose a formal methods approach to reinforcement learning that (i) provides a formal specification language that integrates high-level, rich, task specifications with a priori, domain-specific knowledge; (ii) makes the reward generation process easily interpretable; (iii) guides the policy generation process according to the specification; and (iv) guarantees the satisfaction of the (critical) safety component of the specification. The main ingredients of our computational framework are a predicate temporal logic specifically tailored for robotic tasks and an automaton-guided, safe reinforcement learning algorithm based on control barrier functions. Although the proposed framework is quite general, we motivate it and illustrate it experimentally for a robotic cooking task, in which two manipulators worked together to make hot dogs. Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works INTRODUCTION Robotic systems that are capable of learning from experience have recentlybecome more commonplace. These systems have demonstrated success in learning difficult control tasks. However, as tasks become more complex and the number of options to reason about becomes greater, there is an increasing need to be able to specify the desired behavior in a structured and interpretable fashion, guarantee system safety, and conveniently integrate task-specific knowledge with more general knowledge about the world. This paper addresses these problems specifically in the case of reinforcement learning (RL) by using techniques from formal methods. Experience and prior knowledge shape the way humans make decisions when asked to perform complex tasks. Conversely, robots have had difficulty incorporating a rich set of prior knowledge when solving complex planning and control problems. In RL, the reward offers an avenue for incorporating prior knowledge. However, incorporating such knowledge is not always straightforward using standard reward engineering techniques. This work presents a formal specification language that can combine a base of general knowledge with task specifications to generate richer task descriptions that are interpretable. For example, to make a hot dog at the task level, one needs to grab a sausage, grill it, place the cooked sausage in a bun, apply ketchup, and serve the assembled hot dog. Prior knowledge about the context ofthe task, e.g., sausages can be damaged ifsqueezed too hard, should also be taken into account. Interpretability in RL rewards—easily understanding what the reward function represents and knowing how to improve it—is a key component in understanding the behavior of an RL agent. This property is often missing in reward engineering techniques, which makes it difficult to understand exactly what the implications of the reward function are when tasks become complex. Interpretability of the reward allows for better value alignment between human intent and system objectives, leading to a lower likelihood of reward hacking (1) by the system. The formal specification language presented in this 1Department of Mechanical Engineering, Boston University, Boston, MA, USA. 2Division of Systems Engineering, Boston University, Boston, MA, USA. *Corresponding author. Email: [xli87@bu.edu](xli87@bu.edu) work has the added benefit of being easily interpretable from the beginni

FAQs

Why is interpretability important in reinforcement learning for robotic tasks?

Interpretability helps explain what the reward function represents and how to improve it, aligning human intent with system objectives and reducing the likelihood of reward hacking.

What challenges arise when defining reward functions for complex robotic tasks?

Formally defining reward functions is difficult, and faulty rewards can be exploited by the learning agent, leading to unintended behavior.

How does the proposed framework ensure safety during reinforcement learning and deployment?

It uses a formal specification language to explicitly define undesirable behaviors and applies control barrier functions to enforce satisfaction of the safety specifications.

Oct 23, 2026

Upload by jay