  This paper presents our solution for the Real Robot Challenge (RRC) III, a
competition featured in the NeurIPS 2022 Competition Track, aimed at addressing
dexterous robotic manipulation tasks through learning from pre-collected
offline data. Participants were provided with two types of datasets for each
task: expert and mixed datasets with varying skill levels. While the simplest
offline policy learning algorithm, Behavioral Cloning (BC), performed
remarkably well when trained on expert datasets, it outperformed even the most
advanced offline reinforcement learning (RL) algorithms. However, BC's
performance deteriorated when applied to mixed datasets, and the performance of
offline RL algorithms was also unsatisfactory. Upon examining the mixed
datasets, we observed that they contained a significant amount of expert data,
although this data was unlabeled. To address this issue, we proposed a
semi-supervised learning-based classifier to identify the underlying expert
behavior within mixed datasets, effectively isolating the expert data. To
further enhance BC's performance, we leveraged the geometric symmetry of the
RRC arena to augment the training dataset through mathematical transformations.
In the end, our submission surpassed that of all other participants, even those
who employed complex offline RL algorithms and intricate data processing and
feature engineering techniques.
