Reproduce “Towards Dexterous Embodied Manipulation via Deep Multi-Sensory Fusion and Sparse Expert Scaling” on OpenResearch
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About this paper
Yirui Sun, Guangyu Zhuge, Keliang Liu, Jie Gu, Zhihao xia, Qionglin Ren, Chunxu tian, Zhongxue Ga
The DeMUSE framework introduces a Diffusion Transformer for robotic manipulation that deeply integrates RGB, depth, and 6-axis force feedback with sparse expert scaling. This approach achieves an 83.2% success rate on the augmented MetaWorld MT50 benchmark and demonstrates superior active compliance in real-world contact tasks.
Key insights
- Deep fusion of heterogeneous sensory inputs (RGB, depth, 6-axis force) within a unified denoising flow is critical for internalizing physically consistent latent dynamics and achieving precise, dexterous manipulation.
- Modality-specific normalization (AdaMN) is essential for stabilizing multimodal transformers, preserving the distinct dynamics of diverse sensory cues, and ensuring better world-modeling fidelity and execution precision.
- Sparse Mixture-of-Experts with a shared branch provides an efficient method to scale model capacity, allowing experts to specialize in different aspects (e.g., disentangling physical contact from spatial geometry) without increasing inference latency, crucial for real-time robot control.
Summary by alphaXiv.
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