This paper presents a novel approach for managing failures in collaborative robotics environments. The key highlights are:
The authors extend the BTMG policy representation by introducing adaptable recovery behaviors, which are modeled as specialized robotic skills. These recovery behaviors are designed to address known failure scenarios.
The recovery behavior parameters can be set manually, through reasoning, or optimized using reinforcement learning (RL) to enhance their adaptability and effectiveness.
The authors evaluate their approach through a series of progressively challenging peg-in-a-hole task scenarios, where they introduce various failure cases and demonstrate the necessity for sophisticated recovery behaviors.
The experiments are conducted using a dual-arm KUKA robot, showcasing the approach's ability to adapt to and recover from failures, leading to improved operational efficiency and task success rates.
The authors discuss the role of a planner in their framework, highlighting the challenges of applying planning in real-time collaborative scenarios and the strategy of triggering planning only when necessary to address deviations from expected task execution.
The results demonstrate that the integration of recovery behaviors, modeled as adaptable robotic skills within the BTMG framework, significantly enhances the robot's ability to recover from failures, outperforming traditional automated recovery strategies in terms of adaptability and responsiveness.
The authors outline future work focused on developing a comprehensive recovery pipeline that can automatically identify failures and select the appropriate recovery skills, further enhancing the system's adaptability and resilience in complex environments.
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by Faseeh Ahmad... في arxiv.org 04-10-2024
https://arxiv.org/pdf/2404.06129.pdfاستفسارات أعمق