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title
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RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
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site name
author
updated
2026-02-21 19:33:43
raw text
RT-Sketch RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches Priya Sundaresan, 1, 3 Quan Vuong, 2 Jiayuan Gu, 2 Peng Xu, 2 Ted Xiao, 2 Sean Kirmani, 2 Tianhe Yu, 2 Michael Stark, 3 Ajinkya Jain, 3 Karol Hausman, 1, 2 Dorsa Sadigh*, 2 Jeannette Bohg*, 1 Stefan Schaal*, 3 * Equal advising, alphabetical order 1 Stanford University, 2 Google DeepMind, 3 [Google] Intrinsic Additional Results arXiv Abstract Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like language, but similar to images they can also help a downstream policy to be spatially-aware and even go beyond images to disambiguate task-releva...
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2026-02-21 19:33:43
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