Physical interaction quality is central to deformable-object manipulation, yet most benchmarks evaluate task success alone. A policy may complete the task while allowing slip or causing excessive compression. A primary bottleneck is the absence of visuo-tactile datasets that pair policy-visible contact observations wit...
Bowen Jing, Ming-Xin Wang, Ruiyang Hao et al.· 0 citations
Pretrained generative Diffusion Transformers (DiTs) capture rich pixel-level visual and language-conditioned structure through large-scale image and video generation training. A growing line of robot policies builds on this generative prior, but how it should be transferred to control remains unclear, and existing appr...
Zan-Yi Wang, Yu-Heng Lei, Deng-Yang Jiang et al.· 0 citations
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment-specific sensors. We introduce DexTouch-WM, an action-conditioned world model that learns from scalable human touch to jointly predict futu...
Yan-Jiao Qin, Yue Chen, Wen-Wei Lin et al.· 0 citations
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generati...
Qi-Ze Yu, Lian-Rui Fan, Bo-Yu Chen et al.· 0 citations
MoPA is presented, a framework that aligns perceptual conditioning with mobility and manipulation while preserving coordination at the action level, and achieves state-of-the-art performance across all three task suites.
Guang-Yu Chen, Qi-Wei Liang, Shao-Long Zhu et al.· 2 citations
A reinforcement learning formulation is presented to solve in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture, which shows robustness to state-estimation errors caused by oc- clusion.
Liu-Ao Pei, Tian-Yue Wu, Hui Zhang et al.· 1 citation
Xpolicylab is released as shared infrastructure for reproducible policy comparison and standardized deployment across simulation and physical platforms for reproducible policy comparison and standardized deployment across simulation and physical platforms.
This work proposes COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE, compressing extended context into bounded representations, and evaluates on high-signal dynamic scenarios where historical context is most critical for behavior correctness, and accordingly design behavioral metrics.
Zhixuan Liang, Yuxiao Chen, Yurong You et al.· arXiv.org· 1 citation
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