13 citations · 52 across the 48 of their papers we have counts for
8 papers · 2 filters
Reflex: Visually Grounded Reactive Humanoid Control
Taoyang Jia, Weikai Huang, Linxin Song +6
Humans effortlessly react to the world; they adeptly return a tennis shot, catch a falling cup, step aside from a passing cart, or pass a box tossed during truck unloading. These e…
Grounded Action Model: 3D Grounding as a Foundation for Robotics
Gehao Zhang, Weikai Huang, Shailesh Shailesh +3
Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, from language in vision-languag…
MolmoAct2: Action Reasoning Models for Real-world Deployment
Haoquan Fang, Jiafei Duan, Donovan Clay +26
Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter for real-world deployment.…
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
Abhay Deshpande, Maya Guru, Rose Hendrix +23
A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning
Yalcin Tur, Jalal Naghiyev, Haoquan Fang +4
Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While…
VLS: Steering Pretrained Robot Policies via Vision-Language Models
Shuo Liu, Ishneet Sukhvinder Singh, Yiqing Xu +2
Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarel…
TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics
Shirui Chen, Cole Harrison, Ying-Chun Lee +6
General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled, failed, or partially completed behavior.…