3 citations · 4 across the 4 of their papers we have counts for
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…
Melting Pot 2.0
John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán +14
Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsis…
Statistical discrimination in learning agents
Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao +9
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One pr…