发表机构
University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究共享自主性中工作空间设计对意图推理的影响，通过将其公式化为优化问题并在有界噪声模型下得出概率保证，经模拟实验验证优化布局可提高目标推理可靠性，还展示了集成该框架的现实系统，凸显环境设计的作用。
AI 中文摘要
共享自主性使人类和机器人能够通过将人类输入与自主辅助相结合来协作执行任务。大多数先前工作专注于在固定环境下改善意图推理，而忽略了工作空间设计本身如何影响推理难度。我们发现物体的物理排列直接影响嘈杂用户输入下候选目标的可分离性。我们将工作空间设计公式化为一个优化问题，并在有界噪声模型下得出概率正确性保证。通过多个桌面场景的模拟实验，我们表明与基线布局相比，优化布局提高了目标推理可靠性并减少了模糊性。我们还展示了一个集成了所提出推理框架的现实世界共享自主系统。这突出了环境设计作为改善共享自主系统的补充轴的作用。
英文摘要
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.
CommentsICRA 2026 Workshop Shared Challenges in Human-Centered and Resilient Robotic Autonomy
