MIT's Center for Transportation and Logistics finds sustainability commitments are strong, but governance gaps and external disruptions prevent execution.
This work characterize models from this family of methods as JEPA-WMs and investigates the technical choices that make algorithms from this class work, and proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.
Basile Terver, Tsung-Yen Yang, Jean Ponce et al.· Trans. Mach. Learn. Res.· 49 citations· ⚡7
This work introduces StarVLA-$\alpha, a simple yet strong baseline designed to study VLA design choices under controlled conditions that deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis.
Jinhui Ye, Ning Gao, Sen-Qiao Yang et al.· arXiv.org· 49 citations· ⚡11
The Very Big Video Reasoning (VBVR) Dataset is introduced, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets.
Maijunxian Wang, Rui-Si Wang, Ju-Yi Lin et al.· arXiv.org· 28 citations· ⚡5
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to"play"robots through a compact semantic interface linking intent to action. Show-Harness exposes...
Yan-Zhe Chen, Ze-Chen Bai, Zhi-Jun Cao et al.· 23 citations· ⚡2
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