Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees

2026-07-16

AI Summary

This work addresses a key limitation in traditional shared autonomy systems, which typically assume static environments and overlook how workspace layout affects the difficulty of intention inference. For the first time, this study treats environment design as a critical dimension for enhancing shared autonomy performance and proposes a joint optimization framework that simultaneously optimizes object arrangement to improve the separability of target intentions under noisy inputs. By incorporating a bounded noise model, the framework provides probabilistic correctness guarantees. Integrating layout optimization, intention separability analysis, and probabilistic reasoning, the method significantly enhances intention inference reliability and reduces ambiguity across diverse simulated tabletop scenarios. Real-world robotic experiments further validate the effectiveness of the proposed approach.

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.

Problem

• shared autonomy
• environment design
• intent inference
• goal ambiguity
• workspace layout

Innovation

• shared autonomy
• workspace design
• probabilistic guarantees
• intent inference
• environment optimization