JitterVLA is a replay benchmark designed to evaluate motion quality in vision‑language‑action (VLA) driven human‑robot manipulation. It replays identical command and arrival traces through different controllers to isolate controller effects, and is applied to synthetic reaches, hand trajectories from 20 people, three open VLA pipelines (838 episodes), and a physical UR3 robot. The benchmark demonstrates that interpolation reduces modeled p99 jerk by 83% for human motion with a 200 ms delay, while jerk‑limited generation further lowers jerk at a higher cost, and shows varying effects of entropy‑based techniques across pipelines.
By Faith Ogunwoye, Didar Seyidov, Konstantin Gubernatorov, Dzmitry Tsetserukou
arXiv:2606. 12105v1 Announce Type: cross Abstract: Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate.
By Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga, Moritz Reuss, Gianluca Geraci, Xinkai Jiang, Rudolf Lioutikov
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
Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high-frequency modality changes at hundreds of hertz, vision evolves more slowly, and language stays constant across an episode.
ForeTime‑VLA is a causal vision‑language‑action policy that distills future‑aware representations from a frozen Fast‑WAM teacher, enabling it to anticipate contact events during conveyor‑belt manipulation. The method compresses current and future video latents into a 64‑dimensional target, uses an eight‑frame history encoder to predict this target along with manipulation phase and time‑to‑transition, and conditions a VLM prefix on future tokens and phase. On a deduplicated conveyor‑belt dataset, ForeTime‑VLA reduces test MAE by 2.63% and L2 by 3.02%, while real‑robot experiments show significantly higher grasp success rates compared to the next‑best reference.
whyItMatters":"The approach demonstrates that distilling future‑token knowledge from a world‑action model can improve dynamic manipulation performance without the computational cost of running the teacher at inference time."
By Siyuan Ma, Yutian Zhang, Boshi Zhang, Qinglian Wu, Jiaqi Zhai, Dong Wei, Xiaojin Huang
DeltaWAM introduces a new approach to world-action models (WAMs) for bimanual manipulation by jointly predicting visual deltas and actions instead of dense future frames, thereby reducing redundant modeling of unchanged content and mitigating nuisance appearance variations. The method employs three architectures with varying representation and computation sharing, and incorporates Streaming Delta Memory (SDM) to update cached anchor context using compact observed deltas, which cuts heavy video-expert processing. Experiments on RoboTwin show that DeltaWAM with SDM raises average success rates from 81.3% to 85.4% in clean settings and from 75.8% to 83.9% under visual randomization, while also reducing training FLOPs by up to 23.77% and inference latency by 36.57%.
whyItMatters":"DeltaWAM improves both performance and computational efficiency for bimanual manipulation tasks by focusing on visual deltas and efficient memory updates, as demonstrated by higher success rates and lower FLOPs on RoboTwin."
By Han Yan, Zishang Xiang, Haokai Jiang, Zeyu Zhang, Qilin Wang, Weiyu Guo, Yandong Guo, Boxin Shi, Hao Tang
VioLA is a generalist humanoid control policy that predicts body and hand motion latents instead of joint commands, enabling a pretrained controller to execute these latents on a robot. By aligning human and robot motion in shared latent spaces, the policy can be trained on a large dataset of 140.6 million frames, 93.2% of which are human demonstrations. As a result, VioLA can follow locomotion and manipulation instructions on a real robot zero‑shot, achieving 100% locomotion success and 88.6% manipulation success without task‑specific fine‑tuning.
By Mert Albaba, Jens Bei{\ss}wenger, Anna Manasyan, Daniel Marta, Michael J. Black, Wieland Brendel, Andreas Krause, Georg Martius, Martin Riedmiller
arXiv:2606. 13886v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies.
By Namai Chandra, Shriram Damodaran, Lin Wang
arXiv:2606. 06491v1 Announce Type: cross Abstract: Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion.
By Dong Jing, Jingchen Nie, Tianqi Zhang, Jiaqi Liu, Huaxiu Yao, Zhiwu Lu, Mingyu Ding
ViT-H is a video‑to‑humanoid control framework that generates humanoid reference motions directly from monocular human videos using a diffusion model, eliminating the need for separate reconstruction, retargeting, and tracking stages. The system incorporates motion‑tracking feedback to fine‑tune the generator via preference optimization, and an autoregressive variant enables real‑time teleoperation with sub‑400 ms latency. Experiments on the Unitree G1 robot show that ViT‑H surpasses existing reconstruction‑retargeting‑tracking pipelines in motion quality and executability, and its performance scales positively with model size and data volume.
By Zimo He, Tianyi Bian, Zizhong Yang, Hongjie Li, Nan Jiang, Jieming Cui, Baoxiong Jia, Yizhou Wang, Siyuan Huang, Yixin Zhu
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:2609.24660v2 Announce Type: cross
Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...
By Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao