Formalizing human-robot mutual adaptation: A bounded memory model

Abstract

Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which captures human adaptive behaviors based on a bounded memory assumption. We integrate BAM into a partially observable stochastic model, which enables robot adaptation to the human. When the human is adaptive, the robot will guide the human towards a new, optimal collaborative strategy unknown to the human in advance. When the human is not willing to change their strategy, the robot adapts to the human in order to retain human trust. Human subject experiments indicate that the proposed formalism can significantly improve the effectiveness of human-robot teams, while human subject ratings on the robot performance and trust are comparable to those achieved by cross training, a state-of-the-art human-robot team training practice.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 60 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.