Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesDownload PDF

19 Jun 2021, 10:04 (modified: 04 Nov 2021, 16:27)CoRL2021 PosterReaders: Everyone
Keywords: sim-to-real, offline RL, manipulation, stacking, robot learning
Abstract: We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive policy distillation and simulation-to-reality transfer. Our learned policies can efficiently handle multiple object combinations in the real world and exhibit a large variety of stacking skills. In a large experimental study, we investigate what choices matter for learning such general vision-based agents in simulation, and what affects optimal transfer to the real robot. We then leverage data collected by such policies and improve upon them with offline RL. A video and a blog post of our work are provided as supplementary material.
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