Adaptive mission planning for coupled human-robot teams

Shannon, Christopher J.; Johnson, Luke B.; Jackson, Kimberly F.; How, Jonathan P.

doi:10.1109/acc.2016.7526638

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…A closed-loop planning framework which adapts to varying human parameters and task sets is presented in [9]. Highly coupled tasks between humans and robots are considered and human parameters like task execution time, workload and performance are adopted to update in real-time parameters of human model with the purpose or replanning whenever required.…”

Section: Related Workmentioning

confidence: 99%

A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation

Lippi,

Marino

2021

Preprint

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In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming (MILP) problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents, both human and robotic, are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles; moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed. Simulations in a realistic scenario with two manipulators and a human operator performing an assembly task validate the effectiveness of the approach.

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Section: Related Workmentioning

confidence: 99%

A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation

Lippi,

Marino

2021

Preprint

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“…By not considering the option of delegating tasks to a human, these approaches seek to achieve full autonomy, which is not costeffective in settings where delegation is possible. In contrast, planners for task allocation [8], [9], [10] and adjusting the level of autonomy [11], [12] have been proposed for human-robot teams. The focus on these works is on handling spatiotemporal constraints and different human models [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

Vats¹,

Kroemer²,

Likhachev³

2022

Preprint

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We consider the problem of completing a set of n tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit-how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows exponentially with n -making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using offthe-shelf solvers with bounds on solution quality. To predict the benefit of learning, we use an approximate simulation model of the tasks to train a precondition model that is parameterized by the training task. Finally, we evaluate our approach on peg insertion and Lego stacking tasks-both in simulation and realworld, showing substantial savings in human effort.

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