Keywords: Imitation Learning, Sequence Model, Transformer, Robotics
Abstract: Requirement of human involvement for data collection or system design has always been a major challenge for building robot control policy. In this paper, we present $\textbf{Ro}$bot-$\textbf{BERT}$ (RoBERT), a method to build
general robot control policy for complex behaviors with $\textit{least}$ human effort. Starting from unsupervisedly-collected dataset, RoBERT has no requirements of human labels, high-quality
behavior dataset or accurate information of system model, in contrast to most
other methods for building general robot agent. RoBERT is further pre-trained via $\textit{Masked Action-Inverse-Inference}$ (MAII), a method inspired by
$\textit{Masked Language Modeling}$ (MLM) in BERT-like language models and has potential to enable $\textit{zero-shot}$, $\textit{multi-task}$, $\textit{keyframe-based}$ robot control with little
architectural change and user-friendly interface. In our empirical study, RoBERT
is successfully applied on various types of robots in simulated environment and could generate stable and flexible behaviors to fulfill complex commands.
Supplementary Material: zip
Primary Area: reinforcement learning
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Submission Number: 1700
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