优化环境布局可提升人机协作中目标推断的可靠性。
Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees
- 将环境设计建模为优化问题，提升目标可区分性。
- 在模拟中实现更可靠的意图推断，降低歧义。
- 适合关注人机协作系统设计的研究者。
共享自主使人类与机器人通过结合人工输入与自主辅助协同完成任务。以往工作多聚焦于固定环境下的意图推断优化，忽视了工作空间设计本身对推断难度的影响。我们发现，物体的物理布局直接影响噪声环境下候选目标的可分性。将工作区设计建模为优化问题，并在有界噪声模型下推导出概率正确性保证。通过多个桌面场景的仿真实验，证明优化布局能显著提升目标推断可靠性并减少歧义。进一步构建了集成该推断框架的真实世界共享自主系统，凸显环境设计作为提升共享自主性能的互补维度的重要性。
原文摘要 · Abstract (English)
Shared autonomy enables humans and robots to collaboratively perform tasks by combining human input with autonomous assistance. Most prior work focuses on improving intent inference under a fixed environment, overlooking how workspace design itself affects inference difficulty. We observe that the physical arrangement of objects directly influences the separability of candidate goals under noisy user inputs. We formulate workspace design as an optimization problem and derive a probabilistic correctness guarantee under a bounded noise model. Through simulation experiments across multiple tabletop scenarios, we show that optimized layouts improve goal inference reliability and reduce ambiguity compared to baseline arrangements. We further demonstrate a real-world shared autonomy system that integrates the proposed inference framework. This highlights the role of environment design as a complementary axis for improving shared autonomy systems.
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