机器人在线协作中自适应调整，仅在必要时请求人类帮助。
Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop
- 机器人实时感知人类行为，动态调整策略以促进合作。
- 仅在确保任务进展时才向人类请求反馈，减少干扰。
- 适合人机协同场景，尤其适用于目标不明确的复杂任务。
我们提出一种新型人机逻辑交互框架，使机器人能够在无限时间范围内可靠完成时序逻辑任务，同时与追求独立且未知目标的人类有效协作。该框架结合两项核心能力：(i) 最大化适应性，使机器人在线调整策略，充分利用人类行为实现合作；(ii) 最小可调反馈，仅在必要时在线请求人类协助以保障任务推进。这一平衡有效降低人机干扰，保护人类自主性，并确保在人类目标冲突时机器人任务仍能持续满足。我们在真实世界的积木操作任务（使用Franka Emika Panda机械臂）和Overcooked-AI基准测试中验证了该方法，结果表明其能生成超越现有方法的丰富、自发的协作行为，同时保持强形式化保证。
原文摘要 · Abstract (English)
We present a novel framework for human-robot \emph{logical} interaction that enables robots to reliably satisfy (infinite horizon) temporal logic tasks while effectively collaborating with humans who pursue independent and unknown tasks. The framework combines two key capabilities: (i) \emph{maximal adaptation} enables the robot to adjust its strategy \emph{online} to exploit human behavior for cooperation whenever possible, and (ii) \emph{minimal tunable feedback} enables the robot to request cooperation by the human online only when necessary to guarantee progress. This balance minimizes human-robot interference, preserves human autonomy, and ensures persistent robot task satisfaction even under conflicting human goals. We validate the approach in a real-world block-manipulation task with a Franka Emika Panda robotic arm and in the Overcooked-AI benchmark, demonstrating that our method produces rich, \emph{emergent} cooperative behaviors beyond the reach of existing approaches, while maintaining strong formal guarantees.
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