A Framework to Describe, Analyze and Generate Interactive Motor Behaviors

By N. Jarrassé, Themistoklis Charalambous, Etienne Burdet. 2012

178 citations recorded by the source corpus.

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks.

Subfields: Control and Systems Engineering, Social Psychology

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Citations in the corpus

Learning from Physical Human Corrections, One Feature at a Time (2018, background): “Traditionally, the human’s interactions are treated in one of three ways [9]: as disturbances to be rejected [5, 12, 23], as collisions to be detected and avoided [4], or as operator signals to be followed by switching into a compliant mode [8, 10, 14].”
Learning Robot Objectives from Physical Human Interaction (2017, background): “Several reactive control strategies have been developed to deal with pHRI [1, 2, 3].”
Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019, methodology): “Cobots were introduced firstly as Intelligent Assist Devices ( IAD ) to support the human by utilizing power assistance or force amplification [29–32].”
A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction (2018, background): “[41], and is based on concepts from neuroscience and game theory.”