الباحثون

Teng Ma

المنشورات 2

نسخة أولية وصول مفتوح

Efficient World Action Model Inference with Adaptive Intermediate States

Zhinnan Liu, Haozhi Han, Ruge Zhang وآخرون · 2026

World Action Models (WAMs) enable future-aware control by jointly modeling actions and environment dynamics. However, iterative diffusion or flow inference incurs substantial denoising latency. Prior inference state offers a natural opportunity for acceleration, yet changing planning contexts, observations, and interme …

نسخة أولية وصول مفتوح

Learning a Speed-adaptive Hip Exoskeleton Control Policy Via Sim-to-real Reinforcement Learning

Bin Li, Zhimin Hou, Jiacheng Hou وآخرون · 2026

Providing personalized exoskeleton assistance across varying walking speeds remains challenging. Existing online optimization methods are sample-inefficient, requiring extensive human-in-the-loop (HIL) evaluations to optimize the entire assistive torque profile. Sim-to-real reinforcement learning (RL) offers a promisin …

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