Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must typically be bootstrapped using human demonstration. However, relying on human demonstration necessarily degrades the autonomy of robots that must learn a wide variety of skills over their operational lifetimes. We propose using kinematic motion planning as a completely autonomous, sample efficient way to bootstrap motor skill learning for object manipulation. We demonstrate the use of motion planners to bootstrap
motor skills in two complex object manipulation scenarios with different policy representations: opening a drawer with a dynamic movement primitive representation, and closing a microwave door with a deep neural network policy. We also show how our method can bootstrap a motor skill for the challenging dynamic task of learning to hit a ball off a tee, where a kinematic plan based on treating the scene as static is insufficient to solve the task, but sufficient to bootstrap a more dynamic policy. In all three cases, our method is competitive with human-demonstrated initialization, and significantly outperforms starting with a random policy. This approach enables robots to to efficiently and autonomously learn motor policies for dynamic tasks without human demonstration.
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Self-Supervised Visual Motor Skills via Neural Radiance Fields
In this paper, we propose a novel network architecture for visual imitation learning that exploits neural radiance fields (NeRFs) and key-point correspondence for self-supervised visual motor policy learning. The proposed network architecture incorporates a dynamic system output layer for policy learning. Combining the stability and goal adaption properties of dynamic systems with the robustness of keypoint-based correspondence yields a policy that is invariant to significant clutter, occlusions, lighting conditions changes, and spatial variations in goal configurations. Experiments on multiple manipulation tasks show that our method outperforms comparable visual motor policy learning methods on both in-distribution and out-of-distribution scenarios when using a small number of training samples.
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- Award ID(s):
- 1830597
- PAR ID:
- 10495944
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems
- ISBN:
- 978-1-6654-9190-7
- Page Range / eLocation ID:
- 3712 to 3718
- Subject(s) / Keyword(s):
- LfD, Dynamic system, Phase space model, NeRF,
- Format(s):
- Medium: X
- Location:
- Detroit, MI, USA
- Sponsoring Org:
- National Science Foundation
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