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We propose a learning‐from‐demonstration approach for grounding actions from expert data and an algorithm for using these actions to perform a task in new environments. Our approach is based on an application of sampling‐based motion planning to search through the tree of discrete, high‐level actions constructed from a symbolic representation of a task. Recursive sampling‐based planning is used to explore the space of possible continuous‐space instantiations of these actions. We demonstrate the utility of our approach with a magnetic structure assembly task, showing that the robot can intelligently select a sequence of actions in different parts of the workspace and in the presence of obstacles. This approach can better adapt to new environments by selecting the correct high‐level actions for the particular environment while taking human preferences into account.
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Visual Robot Task Planning
Prospection, the act of predicting the consequences of many possible futures, is intrinsic to human planning and action, and may even be at the root of consciousness. Surprisingly, this idea has been explored comparatively little in robotics. In this work, we propose a neural network architecture and associated planning algorithm that (1) learns a representation of the world useful for generating prospective futures after the application of high-level actions from a large pool of expert demonstrations, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) uses this same representation to evaluate these actions and perform tree search to find a sequence of high-level actions in a new environment. Models are trained via imitation learning on a variety of domains, including navigation, pick-and-place, and a surgical robotics task. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information.
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- Award ID(s):
- 1637949
- PAR ID:
- 10083430
- Date Published:
- Journal Name:
- WORKSHOP ON LEARNING FROM DEMONSTRATIONS FOR HIGH LEVEL ROBOTIC TASKS, AT ROBOTICS SCIENCE AND SYSTEMS
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
