44 papers
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
Chi Zhang, Penglin Cai, Ziheng Xi +6
As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from vision. However, limited by har…
Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models
Wanpeng Zhang, Ye Wang, Hao Luo +6
Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given prediction is reliable. Distributio…
X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models
Boyu Li, Chaoyi Xu, Haoqi Yuan +5
Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-trained on large and diverse…
RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
Chaoyi Xu, Yixuan Jiang, Jiahui Huan +7
Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deploya…
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
Debiased Model-based Representations for Sample-efficient Continuous Control
Jiafei Lyu, Zichuan Lin, Scott Fujimoto +5
Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream off-policy actor-critic lear…