Timely Negotiation and Correction of Shared Intentions With Body Motion
This work addresses the problem of enabling robots to negotiate and correct shared intentions through body motion, which is incremental as it builds on existing dynamical system approaches.
The paper tackles the problem of robot interaction behavior by addressing the dual task of sequencing discrete actions and incorporating information instantly, using a dynamical system based on the stable heteroclinic channel network to modulate motions for timely negotiation and correction. The result is a compact state graph abstraction suitable for reasoning, planning, and inference.
Current robot architectures for modeling interaction behavior are not well suited to the dual task of sequencing discrete actions and incorporating information instantly. Additionally, for communication based on body motion, actions also serve as cues for negotiating interaction alternatives and to enable timely interventions. The paper presents a dynamical system based on the stable heteroclinic channel network, which provides a rich set of parameters to isntantly modulate motions, while maintaining a compact state graph abstraction suitable for reasoning, planning and inference.