Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the current observation. As episodes grow longer, however, retaining the full history becomes increasingly costly, creating a fundamental scalability challenge for memory-aug...
Hao-Xuan Wang, Geng-Yu Zhang, R. Kompella et al.· 0 citations
Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. However, existing Vision-Language-Action (VLA) policies that predict future observations from shared features leave the forecast decoupled from the actions the policy will a...
Zhe Tao, Fei-Ran Wang, Gao-Wen Liu et al.· 0 citations
The results suggest that current VLA benchmarks may exert limited pressure on deep language grounding and compositional instruction understanding, and that future VLA architectures should allocate capacity more deliberately across language, vision, and action components.
Guoheng Sun, Kai Feng, Shwai He et al.· arXiv.org· 0 citations
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