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On-Robot Learning With Equivariant Models
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Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in the context of on-robot policy learning in which a policy must be learned entirely on a physical robotic system without reference to a model, a simulator, or an offline dataset. We focus on applications of Equivariant SAC to robotic manipulation and explore a number of variations of the algorithm. Ultimately, we demonstrate the ability to learn several non-trivial manipulation tasks completely through on-robot experiences in less than an hour or two of wall clock time.
Forward citations
Cited by 2 Pith papers
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Touch begins where vision ends: Generalizable policies for contact-rich manipulation
A localize-then-execute policy that combines vision-language reaching, semantic background augmentation, and residual reinforcement learning with tactile sensing reaches about 90% success on millimeter-precision manip...
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SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception
Grid-parallel translational trajectory symmetries plus sample-time homographies cut on-robot RL wall-clock training 1.37–2.17× versus SERL on three real contact tasks.
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