Deep compositional robotic planners that follow natural language commands
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipu- late objects. Our approach combines a deep network structured according to the parse of a complex command that i...
| Main Authors: | , , |
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| Format: | Article |
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Center for Brains, Minds and Machines (CBMM), Computation and Systems Neuroscience (Cosyne)
2022
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| Online Access: | https://hdl.handle.net/1721.1/141354 |