GROOT Collection GROOT is a research series investigating how self-supervised and weakly supervised learning can be used to train agents that follow instructions. • 3 items • Updated 10 days ago • 2
Generative Evaluation of Complex Reasoning in Large Language Models Paper • 2504.02810 • Published 20 days ago • 14
ROCKET Collection ROCKET is the research series that explores vision-based goal specification methods. • 11 items • Updated 10 days ago • 2
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment Paper • 2503.02505 • Published Mar 4 • 6
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems Paper • 2504.01990 • Published 23 days ago • 256
view article Article Introducing RWKV — An RNN with the advantages of a transformer May 15, 2023 • 21
DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping Paper • 2502.20900 • Published Feb 28 • 9
ROCKET-1: Master Open-World Interaction with Visual-Temporal Context Prompting Paper • 2410.17856 • Published Oct 23, 2024 • 52
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents Paper • 2407.00114 • Published Jun 27, 2024 • 13