The paper benchmarks ten temporal pattern recognition tasks across eighteen neural network architectures, identifying BiGRU, TCN, Conv1D, and GRUReLU as consistently top performers with under 2,000 parameters. These models are then applied to estimate dynamic arm gesture speed from skeletal keypoint sequences, achieving mean absolute errors of 0.198 (peak count), 4% (period time), and 8% (mean spike spacing). The results show that neural networks can reliably predict gesture speed, enabling speed-aware gesture-controlled robotic systems.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
By Federico Del Pup, Elisa Tentori, Manfredo Atzori
The paper introduces a learned speed prior that predicts a distribution over execution speed for robot demonstrations, allowing consistent retiming across tasks. By applying this prior to both robot teleoperation and handheld demonstrations, the authors retime demonstrations to a handheld pace before fine‑tuning a vision‑language‑action policy. Experiments on 120 RoboLab tasks and a real robot show that policies trained with the learned clocks achieve higher success rates and smoother motion, outperforming policies fine‑tuned on original or uniformly accelerated timings.
By Seungyong Moon, Deokjae Lee, Hansang Lee, Munkhtselmeg Battsengel, Youngin Kim, Seonghyeon Yun, Sungjin Jeon, SongEun Kim, Jeongmin Park, Hyun Oh Song
arXiv:2609.16864v1 Announce Type: cross
Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
By Zhenyang Feng, Jimin Heo, Erik B. Sudderth, Unnat Jain
The paper introduces the Sequential Spatio-Temporal Attention Network (SSTAN), a Transformer-based architecture that replaces traditional Graph Convolutional Networks for sign language recognition. SSTAN uses a hierarchical, stacked design with Spatial Multi-Head Attention to model joint relationships within frames and Temporal Multi-Head Attention to capture long-range dependencies across frames, eliminating the need for predefined skeletal graphs. Experiments on large-scale datasets (WLASL, JSL, KSL) show that SSTAN, trained from scratch, achieves state‑of‑the‑art performance in fingerspelling categories and outperforms other skeleton‑only methods on WLASL, highlighting its data efficiency and ability to learn complex spatio‑temporal patterns.
By Koki Hirooka, Abu Saleh Musa Miah, Tatsuya Murakami, Md. Al Mehedi Hasan, Yong Seok Hwang, Jungpil Shin
StreamPI introduces a streaming multimodal temporal modeling framework that enhances Vision‑Language‑Action models by adding temporal reasoning without extra parameters. It anchors each visual observation and language instruction pair as a temporal unit, using bidirectional attention for cross‑modal fusion and causal attention for autoregressive streaming inference. The method employs random‑interval streaming training to improve robustness and leverages the LLM backbone’s length extrapolation to inherit pretrained weights, achieving superior performance over pi0.5 on real‑robot and simulation tasks.
By Zhe Liu, Jinghua Hou, Yuxiang Lu, Zhenya Yang, Xianzhe Fan, Junwei Luo, Junyi Li, Ruihua Han, Zhi Hou, Hengshuang Zhao
arXiv:2609.36416v1 Announce Type: cross
Abstract: Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions....
By Jade Choghari, Pepijn Kooijmans, Mansi Agarwal, Yusuf Umut Ciftci, Aseem Doriwala, Catherine Weaver, Mouli Sivapurapu, Kai Yang, Jackson Lee, Thomas Wolf, Pragna Mannam
Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions. Current manipulation datasets struggle to support t...
arXiv:2607. 14675v1 Announce Type: cross Abstract: Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources.
By Zihan Guo, Xiaoqi Li
The paper introduces CF‑Prune, a training‑free method that prunes future‑frame tokens in pretrained World Action Models (WAMs) to reduce inference latency. By using instruction and action attention during the initial denoising pass, the framework identifies task‑relevant tokens, while an entropy‑based phase identification keeps dense computation in precision phases to avoid performance loss. Experiments on RoboLab, RoboCasa, and real‑world manipulation tasks show consistent speedups—up to 1.33× at 50% pruning—while maintaining task success rates within 1 percentage point of the baseline.
By Baekseung Kim, Junhoo Lee, Sohyeon Hong, Suhyun Jeon, Minkyu Kim, Seungyeon Kim, Donghoon Han, Nojun Kwak
arXiv:2609.36416v2 Announce Type: replace-cross
Abstract: Robots operating in real-world environments must often execute complex, multi-step bimanual tasks over long horizons rather than single, isol...
By Jade Choghari, Pepijn Kooijmans, Mansi Agarwal, Yusuf Umut Ciftci, Aseem Doriwala, Catherine Weaver, Mouli Sivapurapu, Kai Yang, Thomas Wolf, Jackson Lee, Pragna Mannam
PhysBrain 1.5 is a unified vision‑language model that learns to understand physical environments, generate actions, and predict future states by encoding language, end‑effector motion, and dense visual targets as discrete sequences and training them with autoregressive next‑token prediction. The model is pre‑trained on human interaction videos and fine‑tuned on human demonstrations, robot trajectories, and simulated experience, achieving an average score of 72.5 across 28 embodied understanding benchmarks and outperforming other open‑source models on 14 of them. It also demonstrates the ability to produce end‑effector trajectories and predict future scenes with spatially aligned RGB, depth, and robot‑mask outputs.
By DeepCybo Team, Yu Bin, Haipeng Cao, Zheng Chang, Kai Chen, Youning Chen, Kailin Deng, Yichao Du, Xiaotong Fu, Haoyang Ge, Yunlong Guo, Chenliu Hao, Jiyan He, Xuguo He, Yakun Hou, Kai Hu, Cong Huang, Tuopusen Huang, Yu Huang, Hong Li, Peize Li, Shijie Lian, Xiaopeng Lin, Yun Lin, Haibao Liu, Haochen Liu, Qiuzhi Liu, Shengcai Liu, Zhiqiang Liu, Tao Luo, Peng Ren, Shuo Ren, Chaoyi Ruan, Zhaolong Shen, Yukun Shi, Qiyuan Su, Yuxuan Tian, Yining Wang, Changti Wu, Hao Wu, Xueyin Xu, Ruoqi Yang, Zhaoyang Yang, Hang Yuan, Zhaoyang Zeng, Hanwen Zhang, Ruimeng Zhang, Yao Zhang, Yibo Zhang, Yuxiang Zhang, Zhirui Zhang, Ziyi Zhang, Zubin Zheng, Zishen Zhuang