Embodied world models predict the visual consequences of candidate actions before execution. However, existing action-conditioned world models often adopt uniformly weighted visual generation objectives that can be misaligned with embodied prediction needs. Even with explicit motion conditioning, these objectives can u...
Zi-Ying Song, Yu-Chen Liu, Zhuo-Ran Xu et al.· 0 citations
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
This work proposes KnockGS, an interaction-response PhysicalGS framework that estimates the elasticity and density scales of a 3D Gaussian object from its dynamics under a known applied force, and recovers the scales substantially more accurately than response retrieval, global regression, or a fixed default material.
Chenchen Ge, Hanwen Shen, Bowen Jing et al.· 0 citations
GaussianDream demonstrates that training-time current Gaussian reconstruction and future Gaussian prediction provide effective 3D supervision, but its dense VGGT/TGE-based prefix jointly carries state, dynamics, and action-conditioning information.
Yu-Qing Jiang, Zi-Jian Zhang, Wei-Tao Zhou et al.· 1 citation
GaussianWAM is proposed, a training-time representation-enhancement framework that organizes geometric and semantic supervision through a 3D Gaussian field and improves performance on standard LIBERO and shows positive transfer trends on RoboTwin and real-world manipulation.
Zi-Jian Zhang, Yu-Qing Jiang, Wei-Tao Zhou et al.· 2 citations
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