1 citations · 1 across the 8 of their papers we have counts for
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GOPLA: Generalizable Object Placement Learning via Synthetic Augmentation of Human Arrangement
Yao Zhong, Hanzhi Chen, Simon Schaefer +2
Robots are expected to serve as intelligent assistants, helping humans with everyday household organization. A central challenge in this setting is the task of object placement, wh…
Actron3D: Learning Actionable Neural Functions from Videos for Transferable Robotic Manipulation
Anran Zhang, Hanzhi Chen, Yannick Burkhardt +4
We present Actron3D, a framework that enables robots to acquire transferable 6-DoF manipulation skills from monocular, uncalibrated, RGB-only human demonstration videos. Our key id…
VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation
Hanzhi Chen, Boyang Sun, Anran Zhang +2
Future robots are envisioned as versatile systems capable of performing a variety of household tasks. The big question remains, how can we bridge the embodiment gap while minimizin…
FrontierNet: Learning Visual Cues to Explore
Boyang Sun, Hanzhi Chen, Stefan Leutenegger +3
Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping,…