Shared Autonomy Assisted by Impedance-Driven Anisotropic Guidance Field
Sihan Chen, Hang Xu, Yupu Lu, Chen Wang, Benfang Duan + 2 more
TLDR
IAGF-SA enhances shared autonomy by allowing robots to intuitively communicate their intent to humans via an impedance-driven physical guidance field.
Key contributions
- Introduces IAGF-SA, a novel shared autonomy paradigm for improved human-robot collaboration.
- Employs an impedance-driven anisotropic guidance field for embodied, physically-grounded robot intent communication.
- Adaptively modulates robot's dynamic response to human input, enabling intuitive guidance.
- User studies confirm enhanced task performance, human-robot agreement, and user experience.
Why it matters
This paper addresses a critical gap in shared autonomy: robots' inability to intuitively communicate their intent to humans. By enabling mutual understanding through physical interaction, it significantly enhances human-robot collaboration. This leads to improved task performance and a more natural, effective user experience.
Original Abstract
Shared autonomy (SA) enables robots to infer human intent and assist in its achievement. While most research focuses on improving intent inference, it overlooks whether humans can understand the robot's intent in return. Without such mutual understanding, collaboration becomes less effective, degrading user experience and task performance. To address this gap, previous studies have explicitly conveyed the robot intent through additional interfaces, which remain unintuitive and limited in expressiveness. Inspired by impedance control, we propose Impedance-Driven Anisotropic Guidance Field Enhanced Shared Autonomy (IAGF-SA), a novel paradigm that extends SA with an embodied, physically-grounded communication channel. This channel adaptively modulates the robot's dynamic response to human input, enabling intuitive, continuous, physically-grounded robot intent communication while naturally guiding human actions. User studies across three scenarios and two teleoperation interfaces indicate that IAGF-SA improves task performance, human-robot agreement, and subjective experience, thus demonstrating its effectiveness in enhancing human-robot communication and collaboration.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.