5 papers
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…
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…
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…
U-ARM : Ultra low-cost general teleoperation interface for robot manipulation
Yanwen Zou, Zhaoye Zhou, Chenyang Shi +4
We propose U-Arm, a low-cost and rapidly adaptable leader-follower teleoperation framework designed to interface with most of commercially available robotic arms. Our system suppor…
RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction
Zewei Ye, Weifeng Lu, Minghao Ye +4
Vision-Language-Action (VLA) models have recently advanced robotic manipulation by translating natural-language instructions and visual observations into control actions. However,…