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
Imitation learning for robotics depends on human demonstrations, some of which people may later ask to remove. Retraining without them is the natural reference, but its cost grows with policy and dataset scale, motivating cheaper operators that edit a trained policy. Metrics inherited from machine unlearning, such as f...
Jia-Zhuo Li, Yu Zhang, Yi-Ming Fei et al.· 1 citation
Quadruped manipulators enable mobile grasping in complex environments, yet their whole-body control policies remain vulnerable to unreliable onboard visual perception. Existing methods are typically developed under relatively reliable observations and have not systematically examined how occlusion, segmentation-mask dr...
Liang Zhou, Jia-Ming Su, Yang Wei et al.· 0 citations
Semantic-Temporal WAM (ST-WAM) is proposed to improve action robustness by using DINOv3 as a shared semantic representation for future prediction and history retrieval while retaining fine-grained VAE dynamics, demonstrating that semantic-temporal modeling effectively complements pixel-generative dynamics for robust ma...
Mingxin Wang, Bin Hu, Bin Qian et al.· arXiv.org· 5 citations· ⚡1
Experiments show that STDR consistently improves sample efficiency and success rates over multiple baselines, and matches or surpasses handcrafted dense rewards on several challenging tasks, suggesting robustness to visual noise and better-calibrated reward assignment across settings.
Yang Yang, Bingjie Chen, Zihan Wang et al.· arXiv.org· 0 citations
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