arXiv:2608.28693v1 Announce Type: cross
Abstract: Enabling humanoid robots to respond to human speech with synchronized and semantically meaningful gestures is fundamental to natural human-robot inte...
By Zifan Wang, Ziang Ren, Pengyang Shi, Zirui Wang, Chenghuai Lin, Tianze Wang, Zekun Qi, Liangliang Zhao, He Wang, Li Yi
arXiv:2606. 18747v1 Announce Type: cross Abstract: Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.
By Chris Lee, Flora Salim, Benjamin Tag, Francisco Cruz
arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.
By J. M. A. Marcelo, M. Brienza, E. Bugli, L. Comito, D. Nardi, D. D. Bloisi, V. Suriani
arXiv:2606. 19914v1 Announce Type: cross Abstract: Art has long stood as a pivotal expression of human creativity.
By Xuetao Li, Wenke Huang, Mang Ye, Zijian Liu, Jinhua Xie, Jifeng Xuan, Miao Li
ECHO-G is a framework for generating full‑body co‑speech motion for humanoid robots, jointly conditioned on speech audio and timed transcripts. Its Speech‑Grounded Diffusion Transformer (SGDiT) fuses frame‑aligned acoustic features with token‑level linguistic context, preserving distinct granularities while modeling one‑to‑many utterance‑motion relationships directly in robot space. The authors introduce a BEAT2‑derived audio‑text‑robot dataset, a benchmark for co‑speech characteristics, robot‑motion quality, and runtime efficiency, and demonstrate that direct robot‑space generation outperforms human‑motion generation and retargeting pipelines, with joint audio‑text conditioning yielding superior results in both quantitative evaluation and a video‑rating study.
"whyItMatters":"The study provides a new dataset, benchmark, and a demonstrably effective method for generating realistic co‑speech motion directly in robot space, advancing practical humanoid robot interaction."
By Yizhao Li, Pusen Gao, Ming Wang, Shaojie Shen, Shuo Yang, Hao Xu
Recent advances in video generation allow robots to learn manipulation trajectories from generated videos. However, these approaches produce purely kinematic trajectories that lack force information,...