5 citations · 14 across the 19 of their papers we have counts for
19 papers
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.…
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Zijun Lin, Jiafei Duan, Haoquan Fang +4
Recent advances in robotic manipulation have integrated low-level robotic control into Vision-Language Models (VLMs), extending them into Vision-Language-Action (VLA) models. Altho…
MolmoAct: Action Reasoning Models that can Reason in Space
Jason Lee, Jiafei Duan, Haoquan Fang +16
Reasoning is central to purposeful action, yet most robotic foundation models map perception and instructions directly to control, which limits adaptability, generalization, and se…
ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
Ge Yan, Jiyue Zhu, Yuquan Deng +8
This paper introduces ManiFlow, a visuomotor imitation learning policy for general robot manipulation that generates precise, high-dimensional actions conditioned on diverse visual…
RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation
Yi Ru Wang, Carter Ung, Christopher Tan +11
We introduce RoboEval, a structured evaluation framework and benchmark for robotic manipulation that augments binary success with principled behavioral and outcome metrics. Existin…
PointArena: Probing Multimodal Grounding Through Language-Guided Pointing
Long Cheng, Jiafei Duan, Yi Ru Wang +12
Pointing serves as a fundamental and intuitive mechanism for grounding language within visual contexts, with applications spanning robotics, assistive technologies, and interactive…
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Haoquan Fang, Markus Grotz, Wilbert Pumacay +4
Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatia…
RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields
Som Sagar, Jiafei Duan, Sreevishakh Vasudevan +4
Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the real world. Diagnosing these vulnerabi…
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Jiafei Duan, Wilbert Pumacay, Nishanth Kumar +7
Robotic manipulation in open-world settings requires not only task execution but also the ability to detect and learn from failures. While recent advances in vision-language models…
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics
Wentao Yuan, Jiafei Duan, Valts Blukis +5
From rearranging objects on a table to putting groceries into shelves, robots must plan precise action points to perform tasks accurately and reliably. In spite of the recent adopt…
Manipulate-Anything: Automating Real-World Robots using Vision-Language Models
Jiafei Duan, Wentao Yuan, Wilbert Pumacay +4
Large-scale endeavors like and widespread community efforts such as Open-X-Embodiment have contributed to growing the scale of robot demonstration data. However, there is still an…
EVE: Enabling Anyone to Train Robots using Augmented Reality
Jun Wang, Chun-Cheng Chang, Jiafei Duan +2
The increasing affordability of robot hardware is accelerating the integration of robots into everyday activities. However, training a robot to automate a task requires expensive t…
THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
Wilbert Pumacay, Ishika Singh, Jiafei Duan +3
To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a major…
NEWTON: Are Large Language Models Capable of Physical Reasoning?
Yi Ru Wang, Jiafei Duan, Dieter Fox +1
Large Language Models (LLMs), through their contextualized representations, have been empirically proven to encapsulate syntactic, semantic, word sense, and common-sense knowledge.…