5 citations · 14 across the 19 of their papers we have counts for
6 papers · 1 filter
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.…
vla-eval: A Unified Evaluation Harness for Vision-Language-Action Models
Suhwan Choi, Yunsung Lee, Yubeen Park +4
Vision-Language-Action (VLA) models are increasingly evaluated across multiple simulation benchmarks, yet adding each benchmark to an evaluation pipeline requires resolving incompa…
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…
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.…