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Title: Finite-Horizon Synthesis for Probabilistic Manipulation Domains
Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of human moves. The limit on human moves is unintuitive, and in many settings determinism is undesirable. In this paper, we present a novel planning method for collaborative human-robot manipulation tasks via probabilistic synthesis. We introduce a probabilistic manipulation domain that captures the interaction by allowing for both robot and human actions with states that represent the configurations of the objects in the workspace. The task is specified using Linear Temporal Logic over finite traces (LTLf ). We then transform our manipulation domain into a Markov Decision Process (MDP) and synthesize an optimal policy to satisfy the specification on this MDP. We present two novel contributions: a formalization of probabilistic manipulation domains allowing us to apply existing techniques and a comparison of different encodings of these domains. Our framework is validated on a physical UR5 robot.  more » « less
Award ID(s):
1830549
NSF-PAR ID:
10300748
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Robotics and Automation - ICRA
Page Range / eLocation ID:
6336 to 6342
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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