Robot Planning with Mental Models of Co-present Humans

Buckingham, David; Chita-Tegmark, Meia; Scheutz, Matthias

doi:10.1007/978-3-030-62056-1_47

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The environment is represented as a first-order Markov process (implying that the human can be represented as an hidden Markov model acting upon the, observable, environment). Buckingham et al propose a planning scheme questioning humans mental models returning the effects of expected future humans actions [13]. The planner is then able to determine a robot policy influencing the humans actions.…”

Section: B Planning a Task For Both The Human And The Robotmentioning

confidence: 99%

HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

Buisan

Favier

Mayima

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

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The variety and complexity of tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots collaborating with humans. Indeed, humans are either requested for punctual help or are given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach, named Human-Aware Task Planner with Emulation of Human Decisions and Actions (HATP/EHDA), is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human). We present the rationale of our approach along with a formalization and show its potential on an illustrative example.

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Section: B Planning a Task For Both The Human And The Robotmentioning

confidence: 99%

HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

Buisan

Favier

Mayima

et al. 2022

2022 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Talamadupula et al propose to consider the human as a planning agent with a distinct goal and use plan recognition techniques to generate a robot plan coordinated with the human one [18]. Buckingham et al integrate a separate human model taking into account a mental model of the human to generate plan for collaborative, competitive, and non-involved humans indifferently [4]. Finally, Unhelkar et al propose a scheme integrating the human action model as an agent Markov model to predict the human action into a POMDP, allowing the robot to follow policies influencing the human actions through communication [21].…”

Section: Introductionmentioning

confidence: 99%

A Human-Aware Task Planner Explicitly Reasoning About Human and Robot Decision, Action and Reaction

Buisan

Alami

2021

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

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The complexity of the tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots interacting with humans to perform tasks. Indeed, humans are either requested for punctual help or given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human(s)). We present the rationale of our approach along with a formalization and show its potential on an illustrative example involving the assembly of a table by a robot and a human.

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Robot Planning with Mental Models of Co-present Humans

Cited by 2 publications

References 19 publications

HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

A Human-Aware Task Planner Explicitly Reasoning About Human and Robot Decision, Action and Reaction

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