Automated Robot Recovery from Assumption Violations of High-Level Specifications (2407.00562v2)

Abstract: This paper presents a framework that enables robots to automatically recover from assumption violations of high-level specifications during task execution. In contrast to previous methods relying on user intervention to impose additional assumptions for failure recovery, our approach leverages synthesis-based repair to suggest new robot skills that, when implemented, repair the task. Our approach detects violations of environment safety assumptions during the task execution, relaxes the assumptions to admit observed environment behaviors, and acquires new robot skills for task completion. We demonstrate our approach with a Hello Robot Stretch in a factory-like scenario.

• The paper introduces a novel automated framework that detects, relaxes, and repairs assumption violations in robotic high-level specifications.
• It details a three-step approach involving violation detection through Boolean monitoring, assumption relaxation, and skill augmentation for recovery.
• Practical evaluations using a Hello Robot Stretch in simulated factory scenarios demonstrate the framework's effectiveness in adapting to dynamic environmental changes.

Automated Robot Recovery from Assumption Violations of High-Level Specifications

The paper presents a novel framework for enabling robots to autonomously recover from assumption violations in high-level specifications during task execution. This approach addresses a significant limitation in existing synthesis-based methodologies where human intervention is often required to handle unexpected environment behaviors during robotic tasks. The proposed framework facilitates an automatic and dynamic adjustment of the robot's capabilities through the suggestion and addition of new skills.

Summary of the Approach

The framework is built around three main procedures: violation detection, assumption relaxation, and repair with skill augmentation. Initially, a strategy is synthesized from a given high-level specification using Generalized Reactivity(1) (GR(1)), a fragment of Linear Temporal Logic (LTL) known for its relative efficiency in synthesis. The synthesized strategy not only considers system behaviors but also environment assumptions. If these assumptions are violated during execution, the system's guarantees cannot be maintained without modification.

1. Violation Detection: This component uses a Boolean expression-based monitor to observe and identify when the environment's behavior diverges from the specified assumptions. Violations are detected by comparing actual behavior with the predefined constraints.
2. Assumption Relaxation: Upon detecting an assumption violation, the system relaxes the violated constraints to accommodate the observed behaviors while maintaining the system's safety and liveness properties as much as possible. The relaxation process adapts the environment assumptions to allow the robot to resume operations without substantial loss of specification integrity.
3. Synthesis-Based Repair and Skill Augmentation: If relaxation leads to an unrealizable specification, the framework employs an overview-based repair process to propose new robot skills. This involves modifying skill preconditions and postconditions to ensure that altered environment behaviors can be managed. The addition of these skills is key to recovering the strategy's realizability despite evolving circumstances.

Practical Implementation and Results

The paper demonstrates the efficacy of the framework through a physical implementation using a Hello Robot Stretch in a simulated factory scenario. Notably, the framework successfully recovered from multiple assumption violations involving unexpected object movements and changes in user inputs. In each scenario, the framework dynamically adjusted the initial specifications by adding new skills crafted through automated repair processes. These adjustments allowed the robot to continue task execution seamlessly.

Implications and Future Work

This research enhances the robustness and applicability of formal synthesis in real-world environments, marking a significant advancement in autonomous robotics. The practical implications are vast, enabling robots to function more independently in dynamic and unpredictable settings without frequent human interventions.

Potential future directions include improving computational efficiency through localized repair techniques and extending the framework to multi-agent systems and human-robot collaboration contexts. Addressing sensor noise and controller inaccuracies within this framework could further enhance its applicability, ensuring that even unforeseen, non-cooperative environmental adjustments can be autonomously managed by robotic systems.

The paper offers a substantial contribution to the field of robotics by providing a clear pathway towards more resilient and capable autonomous systems, capable of gracefully adapting to changes without human intervention. This adaptability could pave the way for more reliable robotic assistants in a variety of complex environments.