38 citations · 54 across the 5 of their papers we have counts for
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cs.LG2021★ 1 cited
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman +7
The goal of continuous control is to synthesize desired behaviors. In reinforcement learning (RL)-driven approaches, this is often accomplished through careful task reward engineer…
cs.LG2021
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Michael H. Lim, Andy Zeng, Brian Ichter +5
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and generalizability, scaling th…