Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry
arxiv(2023)
摘要
Geometric regularity, which leverages data symmetry, has been successfully
incorporated into deep learning architectures such as CNNs, RNNs, GNNs, and
Transformers. While this concept has been widely applied in robotics to address
the curse of dimensionality when learning from high-dimensional data, the
inherent reflectional and rotational symmetry of robot structures has not been
adequately explored. Drawing inspiration from cooperative multi-agent
reinforcement learning, we introduce novel network structures for single-agent
control learning that explicitly capture these symmetries. Moreover, we
investigate the relationship between the geometric prior and the concept of
Parameter Sharing in multi-agent reinforcement learning. Last but not the
least, we implement the proposed framework in online and offline learning
methods to demonstrate its ease of use. Through experiments conducted on
various challenging continuous control tasks on simulators and real robots, we
highlight the significant potential of the proposed geometric regularity in
enhancing robot learning capabilities.
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