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arXiv · 2409.03332

Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion

Dikai Liu·Tianwei Zhang·Jianxiong Yin·Simon See
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Abstract

With the rising focus on quadrupeds, a generalized policy capable of handling different robot models and sensor inputs becomes 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 mechanism with masking for quadruped locomotion. It employs direct sensor-level attention to enhance the sensory-temporal understanding and handle different combinations of sensor data, serving as a foundation for incorporating unseen information. MSTA can effectively understand its states even with a large portion of missing information, and is flexible enough to be deployed on physical systems despite the long input sequence.

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Dikai Liu, Tianwei Zhang, Jianxiong Yin, Simon See. 2024-09-05. Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion. https://arxiv.org/abs/2409.03332

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