1 citations · 1 across the 8 of their papers we have counts for
8 papers
Robot Self-Improvement via Human-Video Dynamics Models
Hanzhi Chen, Anran Zhang, Simon Schaefer +5
A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, and the experience of failure.…
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,…
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou +4
Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective an…
FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object
Hanzhi Chen, Binbin Xu, Stefan Leutenegger
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categor…
Anthropomorphic Grasping with Neural Object Shape Completion
Diego Hidalgo-Carvajal, Hanzhi Chen, Gemma C. Bettelani +6
The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is wel…