Safety-Critical Learning of Robot Control with Temporal Logic Specifications (2109.02791v7)

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.

Analyzing Safety-Critical Learning in Robotics via Temporal Logic

The paper presents an advanced framework for safety-critical reinforcement learning (RL) in the context of robotic control, articulated through the integration of linear temporal logic (LTL). This framework aims at addressing both the need for safe exploration and optimal policy learning over complex tasks characterized by continuous state-action spaces and uncertain dynamics.

Framework Overview

At its core, the proposed method incorporates several key components:

1. LTL and Automaton Construction: The framework employs LTL to encode complex robotic tasks over infinite horizons. A novel automaton structure, specifically the Embedded-Limit Deterministic Generalized B\"uchi Automaton (E-LDGBA), acts as the backbone for converting LTL formulas. This automaton takes advantage of deterministic transitions in continuous spaces, facilitating the application of deterministic policies.
2. Reward Schemes and Modular Architecture: By devising an innovative reward scheme, the paper ensures probabilistic satisfaction, enhancing the RL agent's performance. The task is decomposed into sub-components through a modular policy-gradient architecture that employs a deep deterministic policy gradient (DDPG) approach. This modular setup mitigates the global variance issue by leveraging distributed neural networks.
3. Gaussian Processes and ECBFs: Uncertain dynamic systems are managed using Gaussian Processes (GPs) to estimate unmodeled disturbances. By combining this with Exponential Control Barrier Functions (ECBFs), the authors introduce a model-based safe exploration policy. This guarantees safety constraints even in high-order systems, effectively rendering the control policy robust against uncertainties.
4. Safe Exploration and Guiding Process: The efficiency of exploration is further boosted by introducing a guiding process linked to LTL automata properties and ECBFs. This process not only confines the RL agent's exploration to safe regions but also reinforces policy exploration within the bounds of safety requirements.

Numerical Results and Claims

This framework demonstrates impressive outcomes in several simulated robotic settings, achieving near-perfect success rates. The methodology assures high probabilistic confidence for maintaining safety during the training phases, effectively showcasing robust application potential in real-world scenarios.

Implications and Forward-Looking Speculations

Theoretically, this research illustrates the strength of hybrid strategies that meld formal methods (LTL, automata) with machine learning techniques (deep RL, GPs). Practically, such a framework could reframe how autonomous systems are validated and verified in operation-critical environments.

Looking forward, one promising avenue of development is the embedding of machine-learning-driven safety layers into physical robots that interact with unpredictable environments. The extension of this approach to handle more diverse and dynamic tasks, possibly in multi-agent settings, offers intriguing possibilities. Furthermore, increasing the adaptability of GPs for faster and more scalable real-time safety assurances could form the next leap in advancement.

In conclusion, this paper offers a comprehensive method for addressing safety and performance in robotic control via LTL, paving the way for methodical deployment in managing real-world complexities.