2 citations · 5 across the 7 of their papers we have counts for
9 papers
KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies
Yihan Zeng, Minghao Ye, Yiyuan Chen +4
Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action depends on previously complet…
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…
EgoMI: Learning Active Vision and Whole-Body Manipulation from Egocentric Human Demonstrations
Justin Yu, Yide Shentu, Di Wu +3
Imitation learning from human demonstrations offers a promising approach for robot skill acquisition, but egocentric human data introduces fundamental challenges due to the embodim…
RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation
Philipp Wu, Yide Shentu, Qiayuan Liao +5
Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demon…
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
Yide Shentu, Philipp Wu, Aravind Rajeswaran +1
Hierarchical control for robotics has long been plagued by the need to have a well defined interface layer to communicate between high-level task planners and low-level policies. W…
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators
Philipp Wu, Yide Shentu, Zhongke Yi +2
Humans can teleoperate robots to accomplish complex manipulation tasks. Imitation learning has emerged as a powerful framework that leverages human teleoperated demonstrations to t…
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
YuXuan Liu, Nikhil Mishra, Maximilian Sieb +3
3D bounding boxes are a widespread intermediate representation in many computer vision applications. However, predicting them is a challenging task, largely due to partial observab…
Learning Instance Segmentation by Interaction
Deepak Pathak, Yide Shentu, Dian Chen +4
We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-…
Zero-Shot Visual Imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo +7
The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm whe…