1 citations · 2 across the 13 of their papers we have counts for
13 papers · 1 filter
KiRoSe: Using Kinematic Data for Efficient Segmentation of Robot Demonstrations
Shuangyu Xie, Kaiyuan Chen, Muhammad Zubair Irshad +3
Segmenting robot demonstration data into disjoint temporal and semantic subsegments is valuable for learning, cleaning, retrieval, and analysis. Segmentation is often performed man…
RoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
Kaiyuan Chen, Shuangyu Xie, Uksang Yoo +9
It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, defo…
QF3: Fast Flow RL with Filtered Q-Gradients
Chung Min Kim, Brent Yi, David McAllister +7
Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for improving pre-trained flow poli…
EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras
Kush Hari, Justin Kerr, Nidhya Shivakumar +7
Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends…
Revisiting the "Push-T" Robot Manipulation Task with Agentic Robotics
Shuangyu Xie, Kaiyuan Chen, Ken Goldberg
Push-T is an iconic benchmark for learning manipulation policies from human demonstrations. The robot must use a single point of contact to push a T-shaped block into a target pose…
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Kaiyuan Chen, Shuangyu Xie, Letian Fu +21
For robots to work reliably in commercial and industrial applications, recent advances in agentic coding systems have potential to combine interpretable robot programming with the…
ASPIRE: Agentic /Skills Discovery for Robotics
Runyu Lu, Yubo Wu, Ethan Kou +13
Traditional robot programming is notoriously challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse environment conf…
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Wenli Xiao, Jia Xie, Tonghe Zhang +14
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of gener…
Playful Agentic Robot Learning
Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…
SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation
Qianzhong Chen, Hau Zheng, Justin Yu +8
Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-quality demonstrations and keep…
WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation
Justin Yu, Andrew Goldberg, Kavish Kondap +7
Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations, retries, and pauses. Prior fram…
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
Letian Fu, Justin Yu, Karim El-Refai +13
"Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied ma…