10 papers
EagleVLA: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference
Zebin Yang, Qi Wang, Yunhe Wang +6
Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks. However, deploying VLA models on low-power onboard devices, such as the Jetson O…
Data Standards for Humanoid Robotics: The Missing Infrastructure for Physical AI
Shaoshan Liu, Xiugong Qin, Xuan Wu +4
The scalability of humanoid robots will depend not only on models and hardware, but also on whether physical experience can accumulate across robots, tasks, organizations, and time…
KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning
Zebin Yang, Tong Xie, Baotong Lu +3
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and envir…
DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation
Zebin Yang, Yijiahao Qi, Tong Xie +3
Vision-Language-Action (VLA) models have shown remarkable success in robotic tasks like manipulation by fusing a language model's reasoning with a vision model's 3D understanding.…
StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
Zhiyuan Chen, Yuxuan Zhong, Fan Wang +4
Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular…
EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval
Zebin Yang, Sunjian Zheng, Tong Xie +6
Object-goal navigation (ObjNav) tasks an agent with navigating to the location of a specific object in an unseen environment. Embodied agents equipped with large language models (L…
In-Context Learning can Perform Continual Learning Like Humans
Liuwang Kang, Fan Wang, Shaoshan Liu +3
Large language models (LLMs) can adapt to new tasks via in-context learning (ICL) without parameter updates, making them powerful learning engines for fast adaptation. While extens…
Context and Diversity Matter: The Emergence of In-Context Learning in World Models
Fan Wang, Zhiyuan Chen, Yuxuan Zhong +8
The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches re…
Conceptual Framework Toward Embodied Collective Adaptive Intelligence
Fan Wang, Shaoshan Liu
Collective Adaptive Intelligence (CAI) represent a transformative approach in embodied AI, wherein numerous autonomous agents collaborate, adapt, and self-organize to navigate comp…
Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations
Shaoshan Liu, Fan Wang, Hongjun Zhou +1
While theory and practice are often seen as separate domains, this article shows that theoretical insight is essential for overcoming real-world engineering barriers. We begin with…