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Recurrent Transformers Compress Observation History for Robotics
Transformers excel at sequential data but struggle with long sequences due to high computational costs. Recurrent Transformers offer a solution by maintaining fixed-size memory, enabling efficient long-horizon streaming vision and robotics tasks like map-free pose estimation.
This approach reduces the impracticality of storing extensive observation histories in robotics, improving real-time processing capabilities. It advances the use of AI in applications requiring continuous, long-term data integration without prohibitive resource demands.
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Score 64
Source Type arxiv
Reposts 0
Topic Quality 57
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