Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913731770843136 |
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| author | Liu, Dikai Zhang, Tianwei Yin, Jianxiong See, Simon |
| author_facet | Liu, Dikai Zhang, Tianwei Yin, Jianxiong See, Simon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion Liu, Dikai Zhang, Tianwei Yin, Jianxiong See, Simon Robotics 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. |
| title | Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.03332 |