Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations

• URL: http://arxiv.org/abs/2203.03797v1
• Date: Tue, 8 Mar 2022 01:36:48 GMT
• Title: Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations
• Authors: Junchi Liang, Bowen Wen, Kostas Bekris and Abdeslam Boularias
• Abstract summary: This paper describes a new neural network-based framework for learning simultaneously low-level policies and high-level policies. A key feature of the proposed approach is that the policies are learned directly from raw videos of task demonstrations. Empirical results on object manipulation tasks with a robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks.
• Score: 13.864448233719598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person. The sub-tasks consist of moving the robot's end-effector until it reaches a sub-goal region in the task space, performing an action, and triggering the next sub-task when a pre-condition is met. Most prior work in this domain has been concerned with learning only low-level tasks, such as hitting a ball or reaching an object and grasping it. This paper describes a new neural network-based framework for learning simultaneously low-level policies as well as high-level policies, such as deciding which object to pick next or where to place it relative to other objects in the scene. A key feature of the proposed approach is that the policies are learned directly from raw videos of task demonstrations, without any manual annotation or post-processing of the data. Empirical results on object manipulation tasks with a robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks, and outperforms popular imitation learning algorithms.

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