Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees
For shared autonomy systems, this paper introduces environment design as a complementary axis to improve inference reliability, though results are limited to simulation and a single real-world demonstration.
This work optimizes workspace layouts to improve goal inference reliability in shared autonomy, achieving reduced ambiguity and probabilistic correctness guarantees under bounded noise.
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.