PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction
Yinhuai Wang Jing Lin Ailing Zeng Zhengyi Luo Jian Zhang Lei Zhang
Humans interact with objects all the time. Enabling a humanoid to learn
human-object interaction (HOI) is a key step for future smart animation and
intelligent robotics systems. However, recent progress in physics-based HOI
requires carefully designed task-specific rewards, making the system unscalable
and labor-intensive. This work focuses on dynamic HOI imitation: teaching
humanoid dynamic interaction skills through imitating kinematic HOI
demonstrations. It is quite challenging because of the complexity of the
interaction between body parts and objects and the lack of dynamic HOI data. To
handle the above issues, we present PhysHOI, the first physics-based whole-body
HOI imitation approach without task-specific reward designs. Except for the
kinematic HOI representations of humans and objects, we introduce the contact
graph to model the contact relations between body parts and objects explicitly.
A contact graph reward is also designed, which proved to be critical for
precise HOI imitation. Based on the key designs, PhysHOI can imitate diverse
HOI tasks simply yet effectively without prior knowledge. To make up for the
lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset
that contains eight whole-body basketball skills. We validate PhysHOI on
diverse HOI tasks, including whole-body grasping and basketball skills.