LAG-Fusion: Asynchronous Multimodal Diffusion Policy for Robotics
Researchers introduce LAG-Fusion, a latency-aware guidance fusion framework that enables multimodal diffusion policies in robotics to operate asynchronously, with each modality running at its native inference rate. The approach uses a reference-frame rebasing rule to align delayed guidance from different modalities before fusion. In experiments on contact-rich manipulation tasks, LAG-Fusion demonstrates improved responsiveness and task performance compared to synchronous fusion and force-aware baselines.
Why it matters: This work addresses a key challenge in robotic imitation learning by enabling efficient and flexible fusion of modalities with differing sensing rates and latencies, which is important for real-world manipulation tasks.
Full story at: arXiv Robotics ↗