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FALCON: Actively Decoupled Visuomotor Policies for Loco-Manipulation with Foundation-Model-Based Coordination
Chengyang He, Ge Sun, Yue Bai +3
We present FoundAtion-model-guided decoupled LoCO-maNipulation visuomotor policies (FALCON), a framework for loco-manipulation that combines modular diffusion policies with a visio…
MGTraj: Multi-Granularity Goal-Guided Human Trajectory Prediction with Recursive Refinement Network
Ge Sun, Jun Ma
Accurate human trajectory prediction is crucial for robotics navigation and autonomous driving. Recent research has demonstrated that incorporating goal guidance significantly enha…
HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation by a Hexapod Robot
Xinrong Yang, Peizhuo Li, Hongyi Li +8
In nature, animals often need to move/manipulate objects comparable in weight/size to their own bodies. Compared to grasping and carrying, pushing provides a more straightforward a…
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems
Lidong Zhai, Zhijie Qiu, Lvyang Zhang +5
This paper proposes the "Academy of Athens" multi-agent seven-layer framework, aimed at systematically addressing challenges in multi-agent systems (MAS) within artificial intellig…
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Peizhuo Li, Hongyi Li, Ge Sun +7
Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use p…