Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion
<|reference_start|>Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion: With the rising focus on quadrupeds, a generalized policy capable of handling different robot models and sensory inputs will be highly beneficial. Although several methods have been proposed to address different morphologies, it remains a challenge for learning-based policies to manage various combinations of proprioceptive information. This paper presents Masked Sensory-Temporal Attention (MSTA), a novel transformer-based model with masking for quadruped locomotion. It employs direct sensor-level attention to enhance sensory-temporal understanding and handle different combinations of sensor data, serving as a foundation for incorporating unseen information. This model can effectively understand its states even with a large portion of missing information, and is flexible enough to be deployed on a physical system despite the long input sequence.<|reference_end|>
@article{liu2024masked,
    title={Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped
  Locomotion},
    author={Dikai Liu, Tianwei Zhang, Jianxiong Yin, Simon See},
    journal={arXiv preprint arXiv:2409.03332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03332},
    primaryClass={cs.RO}
}