31 citations · 106 across the 28 of their papers we have counts for
6 papers · 1 filter
GraspXL: Generating Grasping Motions for Diverse Objects at Scale
Hui Zhang, Sammy Christen, Zicong Fan +2
Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions. More importantly,…
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
Sammy Christen, Lan Feng, Wei Yang +3
Vision-based human-to-robot handover is an important and challenging task in human-robot interaction. Recent work has attempted to train robot policies by interacting with dynamic…
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Jona Braun, Sammy Christen, Muhammed Kocabas +2
We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-obje…
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
Hui Zhang, Sammy Christen, Zicong Fan +4
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the g…
Learning Human-to-Robot Handovers from Point Clouds
Sammy Christen, Wei Yang, Claudia Pérez-D'Arpino +3
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has m…
Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation
Nikola Vulin, Sammy Christen, Stefan Stevsic +1
In this paper we address the challenge of exploration in deep reinforcement learning for robotic manipulation tasks. In sparse goal settings, an agent does not receive any positive…