Recover: A Neuro-Symbolic Framework for Robust Failure Detection and Recovery in Robotic Task Execution

Core Concepts

Recover, a neuro-symbolic framework, leverages ontologies, logical rules, and large language models to efficiently detect failures during robotic task execution and generate recovery plans in real-time, enhancing the reliability and adaptability of autonomous systems.

Abstract

The paper introduces Recover, a neuro-symbolic framework for online failure identification and recovery in robotic task execution. Recover integrates ontologies, logical rules, and large language models (LLMs) to enhance the ability of LLMs to generate recovery plans and decrease the associated costs. The key highlights are: Recover utilizes an ontology, OntoThor, to describe the AI2Thor simulator environment, including objects, actions, spatial relations, and potential failures. This symbolic representation enables the robot to map multi-modal data to the same representation, allowing simultaneous reasoning across all data types. Recover employs a set of logical rules defined in the ontology to accurately detect failures during task execution. The rules cover various failure types, including agent failures, environmental failures, planning failures, preference violations, and safety issues. When a failure is identified, Recover uses an LLM as a re-planner to generate a new plan to recover from the failure and complete the task. By incorporating the symbolic information from the ontology, Recover can steer the LLM-planner towards generating fewer hallucinations and ensuring the system operates within the confines of available objects and actions. Recover operates in an online manner, detecting failures during task execution and generating a new plan based on the current environment conditions, without the need to observe the effects of the failure throughout the entire plan. The experiments demonstrate that Recover's rule-based sub-goal verifier achieves 100% accuracy in failure detection, and its ontology-enhanced LLM re-planning pipeline outperforms a purely LLM-based approach in both success rate and cost-effectiveness.

Stats

Recover demonstrated a significant reduction in monetary costs compared to a purely LLM-based approach.

Quotes

"By incorporating symbolic information, we can steer the LLM-planner towards generating fewer hallucinations, thereby ensuring that the system operates within the confines of available objects and actions." "Recover detects failures during task execution and generates a new plan based on the environment conditions at the moment of failure, without needing to observe the effects of the failure throughout the entire plan."

Key Insights Distilled From

Recover

by Cristina Cor... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00756.pdf

Deeper Inquiries

How can the Recover framework be extended to handle nested failures, where failures occur during the recovery plan execution

To handle nested failures in the Recover framework, where failures occur during the recovery plan execution, an extension can be implemented by incorporating a recursive failure detection and recovery mechanism. This would involve modifying the existing failure detection rules to include checks during the execution of the recovery plan. Recursive Failure Detection: The system can continuously monitor the execution of the recovery plan and apply the same ontology-based reasoning and rule-based failure detection mechanisms to identify any new failures that arise during the recovery process. Adaptive Recovery Planning: Upon detecting a nested failure, the system can dynamically adjust the recovery plan by integrating the new failure type and generating alternative recovery strategies. This adaptive planning process would involve re-evaluating the environment state, available actions, and the overall task goal to generate a revised plan. Feedback Loop: Implementing a feedback loop between the failure detection module and the recovery planning module would enable the system to iteratively refine the recovery strategies based on the evolving environment conditions and encountered failures. Hierarchical Failure Classification: Introducing a hierarchical classification of failures can help prioritize nested failures based on their severity and impact on the task execution. This hierarchical approach would guide the system in addressing critical failures first before tackling less consequential ones. By incorporating these enhancements, the Recover framework can effectively handle nested failures, ensuring robustness and adaptability in the face of complex task execution scenarios.

How can the ontology-based reasoning be further integrated with the LLM-based planning to improve the overall robustness and explainability of the system

Integrating ontology-based reasoning with LLM-based planning can significantly enhance the overall robustness and explainability of the system in the following ways: Semantic Alignment: By aligning the ontology with the language model, the system can ensure that the planning and reasoning processes are grounded in a shared semantic understanding of the environment and task requirements. This alignment facilitates more accurate interpretation of the task context and improves the generation of coherent and contextually relevant plans. Explainable Planning: The ontology can provide a structured representation of the environment, task constraints, and failure scenarios, enabling the system to generate more explainable plans. By leveraging the ontology to guide the LLM in generating plans, the system can produce transparent and interpretable decision-making processes. Hybrid Reasoning: Combining ontology-based symbolic reasoning with LLM-based probabilistic reasoning allows the system to leverage the strengths of both approaches. The ontology provides domain-specific knowledge and constraints, while the LLM offers the flexibility and adaptability to generate context-aware plans. Error Correction: The integration of ontology-based reasoning can help identify and correct errors or inconsistencies in the LLM-generated plans. By cross-referencing the plan with the ontology, the system can detect discrepancies and refine the plan to ensure logical coherence and task feasibility. Personalization and Adaptation: The ontology can store personalized information and domain-specific knowledge, enabling the system to adapt its planning and reasoning processes to individual preferences or specialized task requirements. This personalization enhances the system's effectiveness and versatility in diverse real-world applications. By enhancing the integration between ontology-based reasoning and LLM-based planning, the Recover framework can achieve a more robust, adaptive, and explainable system for failure detection and recovery in complex task environments.

What other real-world applications, beyond the kitchen scenario, could benefit from the Recover framework, and how would the ontology need to be adapted to those domains

The Recover framework, with its ontology-based approach to failure detection and recovery, can be applied to various real-world applications beyond the kitchen scenario. To adapt the ontology to different domains, the following modifications and extensions may be necessary: Healthcare Support: In healthcare settings, the ontology can be extended to include medical equipment, patient preferences, and safety protocols. Failure types related to patient care, medication administration, and emergency response can be incorporated, allowing the system to detect and recover from critical failures in healthcare tasks. Automated Transportation: For applications in automated transportation systems such as autonomous vehicles or drones, the ontology can include road infrastructure, traffic regulations, and vehicle-specific constraints. Failures related to navigation, collision avoidance, and route planning can be defined, enabling the system to ensure safe and efficient transportation operations. Household Assistance: Extending the ontology for household assistance scenarios like cleaning robots or smart home devices would involve incorporating information about home layout, appliance interactions, and user preferences. Failures related to task completion, object manipulation, and user-specific requirements can be defined to support effective failure detection and recovery in household tasks. Industrial Automation: In industrial settings, the ontology can capture information about manufacturing processes, machinery configurations, and safety protocols. Failures related to equipment malfunction, production delays, and quality control issues can be included, allowing the system to address failures in industrial automation tasks effectively. By adapting the ontology to these diverse domains and incorporating domain-specific knowledge and failure scenarios, the Recover framework can be tailored to address a wide range of real-world applications, enhancing reliability, adaptability, and safety in autonomous systems.