让机器人用托盘水壶分担任务，更懂人类家里的用法。
Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning
- 用托盘、水壶等常见物品分担任务，降低机器人规划难度。
- 实验显示新方法比传统方式减少任务执行开销，决策更高效。
- 适合做家庭服务机器人的开发者，提升机器人可解释性。
本文提出一种名为可解释责任分担（Interpretable Responsibility Sharing, IRS）的新启发式方法，用于提升家用机器人在任务与运动规划（TAMP）中的效率。该方法利用家庭环境中常见的辅助物体（如托盘、水壶），通过责任分担机制将复杂任务拆解为可管理的子问题，使机器人更符合人类使用习惯。其核心是优化规则生成（ORS）技术，确保辅助物体的使用具有策略性和情境感知能力。实验表明，该方法在多种家庭任务中显著优于传统方法，不仅降低任务执行成本，还增强了规划过程的可解释性与适应性。代码已开源于 https://github.com/asyncs/IRS。
原文摘要 · Abstract (English)
This article introduces a novel heuristic for Task and Motion Planning (TAMP) named Interpretable Responsibility Sharing (IRS), which enhances planning efficiency in domestic robots by leveraging human-constructed environments and inherent biases. Utilizing auxiliary objects (e.g., trays and pitchers), which are commonly found in household settings, IRS systematically incorporates these elements to simplify and optimize task execution. The heuristic is rooted in the novel concept of Responsibility Sharing (RS), where auxiliary objects share the task's responsibility with the embodied agent, dividing complex tasks into manageable sub-problems. This division not only reflects human usage patterns but also aids robots in navigating and manipulating within human spaces more effectively. By integrating Optimized Rule Synthesis (ORS) for decision-making, IRS ensures that the use of auxiliary objects is both strategic and context-aware, thereby improving the interpretability and effectiveness of robotic planning. Experiments conducted across various household tasks demonstrate that IRS significantly outperforms traditional methods by reducing the effort required in task execution and enhancing the overall decision-making process. This approach not only aligns with human intuitive methods but also offers a scalable solution adaptable to diverse domestic environments. Code is available at https://github.com/asyncs/IRS.
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