Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees
This paper proposes optimizing physical workspace layouts to improve goal inference reliability in shared autonomy systems, providing probabilistic correctness guarantees. Experiments show optimized layouts reduce ambiguity and enhance inference accuracy.
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[Submitted on 16 Jul 2026]
Title:Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees
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Abstract: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.
Comments: ICRA 2026 Workshop Shared Challenges in Human-Centered and Resilient Robotic Autonomy
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.15487 [cs.RO]
(or arXiv:2607.15487v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.15487
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yi-Shiuan Tung [view email] [v1] Thu, 16 Jul 2026 22:23:20 UTC (50,784 KB)
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