[Paper Review] Safety-Critical Learning of Robot Control with Temporal Logic Specifications

Mingyu Cai, Cristian-Ioan Vasile|arXiv (Cornell University)|Sep 7, 2021

Receptor Mechanisms and Signaling4 citations

TL;DR

This paper proposes a safety-critical deep reinforcement learning framework for robotic control using Linear Temporal Logic (LTL) specifications, integrating Gaussian Processes for uncertainty estimation and Exponential Control Barrier Functions (ECBFs) to ensure safe exploration. The method achieves near-perfect success rates with high-probability safety guarantees in continuous-state robotic tasks through a modular policy-gradient architecture with reward shaping and a safe guiding process.

ABSTRACT

Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties. The learning problem becomes even more difficult for complex tasks over continuous state-action. In this paper, we propose a learning-based robotic control framework consisting of several aspects: (1) we leverage Linear Temporal Logic (LTL) to express complex tasks over infinite horizons that are translated to a novel automaton structure; (2) we detail an innovative reward scheme for LTL satisfaction with a probabilistic guarantee. Then, by applying a reward shaping technique, we develop a modular policy-gradient architecture exploiting the benefits of the automaton structure to decompose overall tasks and enhance the performance of learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate the uncertain dynamic systems, we synthesize a model-based safe exploration during the learning process using Exponential Control Barrier Functions (ECBFs) that generalize systems with high-order relative degrees; (4) to further improve the efficiency of exploration, we utilize the properties of LTL automata and ECBFs to propose a safe guiding process. Finally, we demonstrate the effectiveness of the framework via several robotic environments. We show an ECBF-based modular deep RL algorithm that achieves near-perfect success rates and safety guarding with high probability confidence during training.

Motivation & Objective

• To address the challenge of safe exploration in deep reinforcement learning for robotic systems with unknown dynamics and measurement uncertainties.
• To enable learning of complex, high-level tasks specified via Linear Temporal Logic (LTL) over infinite horizons in continuous state-action spaces.
• To integrate formal methods (LTL) with data-driven learning (deep RL) while guaranteeing safety during training through model-based uncertainty estimation.
• To develop a safe guiding process that preserves optimal policies while enforcing safety constraints using ECBFs and LTL automata.
• To demonstrate high-performance, safe control in robotic environments with probabilistic safety guarantees under model uncertainty.

Proposed method

• Translating LTL specifications into a novel automaton structure (LDGBA) to represent complex, infinite-horizon tasks.
• Designing a reward shaping scheme based on the LTL automaton to guide policy learning with probabilistic guarantees of LTL satisfaction.
• Integrating Gaussian Processes (GPs) to estimate uncertain robot dynamics online with probabilistic confidence bounds.
• Employing Exponential Control Barrier Functions (ECBFs) to generalize safety constraints for systems with high-order relative degrees.
• Constructing a safe guiding process that leverages LTL automaton structure and ECBF constraints to steer exploration toward safe, high-reward regions.
• Implementing a modular deep deterministic policy gradient (DDPG) architecture that decomposes tasks and enhances learning efficiency and safety.

Experimental results

Research questions

• RQ1 How can complex, high-level robotic tasks specified in Linear Temporal Logic (LTL) be effectively encoded and learned in continuous state-action spaces?
• RQ2 What reward shaping mechanism ensures probabilistic satisfaction of LTL specifications during deep reinforcement learning?
• RQ3 How can model uncertainty in robotic systems be estimated online with probabilistic confidence to enable safe exploration?
• RQ4 In what way can Exponential Control Barrier Functions (ECBFs) be used to generalize safety constraints for systems with high-order relative degrees?
• RQ5 Can a safe guiding process preserve optimal policies while enforcing safety during deep RL training?

Key findings

• The proposed framework achieves near-perfect success rates in robotic control tasks with high-probability safety guarantees during training.
• The integration of LTL automata with reward shaping enables effective learning of complex, infinite-horizon specifications in continuous environments.
• The use of Gaussian Processes provides online, non-parametric estimation of uncertain dynamics with probabilistic confidence, enabling robust model-based safety.
• Exponential Control Barrier Functions (ECBFs) successfully generalize safety constraints to systems with high-order relative degrees, ensuring safe exploration.
• The safe guiding process preserves the original optimal policies by leveraging LTL automata and ECBF constraints, preventing deviation from optimal behavior.
• The modular DDPG architecture with reward shaping and safety layers demonstrates superior performance and safety in multiple robotic environments.