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cs.RO2026
Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous Manipulation
Bing Cheng, Yiran Mao, Zewei Ye +10
General-purpose dexterous manipulation requires policies to transfer knowledge from related tasks to new tasks with only a few target demonstrations. Existing benchmarks often prov…
cs.RO2026
LA4VLA: Learning to Act without Seeing via Language-Action Pretraining
Tao Lin, Yuxin Du, Yiran Mao +13
Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…
cs.CV2026
Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Tao Lin, Yuxin Du, Jiting Liu +14
Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…