A General Task and Motion Planning Framework For Multiple Manipulators

Pan, Tianyang; Wells, Andrew M.; Shome, Rahul; Kavraki, Lydia E.

doi:10.1109/iros51168.2021.9636119

Cited by 21 publications

(23 citation statements)

References 20 publications

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“…A number of works have tried to deal with the problem of adapting a robot's behavior to the order of execution of a task by a human and many techniques have been proposed to generate behaviors more or less dynamically. These are the techniques (in nonexhaustive list): Planning and Scheduling [4,14,15,17], AND-OR Graphs [8], Behavior-Tree [18,22], Hierarchical Task Networks (HTN) [2,6,13], Game Theory [5], Markov Decision Process (MDP), or a combination of them [21]. Among these techniques, task planning and more precisely hierarchical planning is widely used and very effective to generate symbolic plans in a similar way of the human decomposition of complex tasks into simpler actions [3].…”

Section: Related Workmentioning

confidence: 99%

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

HMEDAN

Kilgus

Fiorino

et al. 2022

FLAIRS

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In this paper, we address flexible assembly plans generation to accommodate human task variability. Unlike existing approaches, the proposed approach is based on a free-style human-robot interaction (HRI) that does not impose any task order on the participants and furthermore can accommodate their errors and help them to correct the errors without stopping the whole assembly process. Our approach is implemented in a real robotic architecture that combines sensory-motor modules with Hierarchical Task Networks (HTN) to endow the cobot with the necessary adaptability to correspond to human actions dynamically. We show experimentally with 56 participants on a simulated industrial assembly task that the cobot increases the task performance (reduction in the number of errors and gestures) without increasing the participants' cognitive load.

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Section: Related Workmentioning

confidence: 99%

Adapting Cobot Behavior to Human Task Ordering Variability for Assembly Tasks

HMEDAN

Kilgus

Fiorino

et al. 2022

FLAIRS

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“…There has been work in multi-robot domains on general task and motion planning [11], [16], [17]. We focus on a subclass of these problems, where we wish to move objects in the presence of movable obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…2 presents an overview of our framework. We compare our framework with two state-of-the-art baselines, namely, a general MR-TAMP framework [11] and a multi-robot extension of the ResolveSpatialConstraints (RSC) algorithm [8]. We show that our framework can solve MR-GTAMP problem instances that are challenging for the baseline methods.…”

Section: Introductionmentioning

confidence: 99%

“…Our work makes the following assumptions, which are common in MR-TAMP [7], [11]: (i) it considers only monotone instances of the MR-GTAMP problem, where each object is moved only once; and (ii) the robots synchronously start and stop the executions of actions. We plan to relax these assumptions in future work.…”

Section: Introductionmentioning

confidence: 99%

“…Then, for each pair of a robot R ∈ R and its grasp g M,R ∈ Gr M,R , we find all partially grounded pick-and-place actions ā that move object M to its target region Re M with R as the pick robot (Alg. 1, line [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For each partially grounded pick-and-place action ā, we find all movable objects that block the pick action of ā and add the corresponding block-pick edges (Alg.…”

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confidence: 99%

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A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Zhang¹,

Chan²,

Zhong³

et al. 2022

Preprint

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We address multi-robot geometric task-andmotion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot as well as information about whether two robots can perform a handover action by calling motionplanning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-andmotion plans. The search process for collaborative task-andmotion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging GTAMP domains and show that it can generate high-quality task-andmotion plans with respect to the planning time, the resulting plan length and the number of objects moved compared to two state-of-the-art baselines.

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