103 citations · 105 across the 10 of their papers we have counts for
14 papers
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
Mingtong Zhang, Dhruv Shah
Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we pr…
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…
D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
Haozhe Lou, Mingtong Zhang, Haoran Geng +9
Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real…
LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
Lihan Zha, Asher J. Hancock, Mingtong Zhang +5
A long-standing goal in robotics is a generalist policy that can be deployed zero-shot on new robot embodiments without per-embodiment adaptation. Despite large-scale multi-embodim…
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Kanav Arora +11
A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
Rong Xue, Jiageng Mao, Mingtong Zhang +1
Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning. While recent rectified flow approaches have advanced visuomotor polic…
RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning
Haoran Geng, Feishi Wang, Songlin Wei +34
Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges i…
KUDA: Keypoints to Unify Dynamics Learning and Visual Prompting for Open-Vocabulary Robotic Manipulation
Zixian Liu, Mingtong Zhang, Yunzhu Li
With the rapid advancement of large language models (LLMs) and vision-language models (VLMs), significant progress has been made in developing open-vocabulary robotic manipulation…
Learning from Massive Human Videos for Universal Humanoid Pose Control
Jiageng Mao, Siheng Zhao, Siqi Song +7
Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperat…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling
Mingtong Zhang, Kaifeng Zhang, Yunzhu Li
Videos of robots interacting with objects encode rich information about the objects' dynamics. However, existing video prediction approaches typically do not explicitly account for…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
DFields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Rearrangement
Yixuan Wang, Mingtong Zhang, Zhuoran Li +5
Scene representation is a crucial design choice in robotic manipulation systems. An ideal representation is expected to be 3D, dynamic, and semantic to meet the demands of diverse…
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields
Mingtong Zhang, Shuhong Zheng, Zhipeng Bao +2
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused…