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LaTTe: Language Trajectory TransformEr. (arXiv:2208.02918v2 [cs.RO] UPDATED)
Aug. 10, 2022, 1:11 a.m. | Arthur Bucker, Luis Figueredo, Sami Haddadin, Ashish Kapoor, Shuang Ma, Sai Vemprala, Rogerio Bonatti
cs.LG updates on arXiv.org arxiv.org
Natural language is one of the most intuitive ways to express human intent.
However, translating instructions and commands towards robotic motion
generation, and deployment in the real world, is far from being an easy task.
Indeed, combining robotic's inherent low-level geometric and kinodynamic
constraints with human's high-level semantic information reinvigorates and
raises new challenges to the task-design problem -- typically leading to task
or hardware specific solutions with a static set of action targets and
commands. This work instead proposes …
More from arxiv.org / cs.LG updates on arXiv.org
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