arXiv AI
Aug 7

SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation

arXiv:2608. 05970v1 Announce Type: cross Abstract: Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks.

By Changyuan Wang, Chubin Zhang, Zhenyu Wu, Runhao Li, Angyuan Ma, Ke Chao, Yinan Liang, Xiuwei Xu, Ziwei Wang, Yansong Tang, Jiwen Lu
arXiv Machine Learning
Sep 17

DSD: Learning Diverse and Reusable Motor Skills via Diffusion Skill Discovery

The paper introduces Diffusion Skill Discovery (DSD), a method that employs a diffusion model to approximate the entropy gradient of policy-induced state distributions via score matching. This approach encourages the learning of a diverse repertoire of motor skills for high‑dimensional humanoid control, overcoming limitations of prior mutual‑information based methods that rely on indirect state entropy estimates. The discovered skills are effectively reused in hierarchical control and zero‑shot tasks, yielding more complex and agile behaviors than previous skill discovery techniques.

By Sun Woo Kim, Xue Bin Peng
arXiv AI
Jul 28

Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface

arXiv:2607. 24023v1 Announce Type: new Abstract: Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics.

By Jiyu Wei, Di Hong, Zhanjie Zhang, Dazhong Rong, Qinming He, Yueming Wang