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.

这项研究针对动态人机交互进行了物理模拟，通过模仿人机动态交互示范来教授仿人机器人动态交互技能，采用了无需任务特定奖励设计的基于物理的全身交互示范方法，通过引入接触图来明确建模身体部位与物体之间的接触关系，并设计了接触图奖励，能够简单而有效地模仿各种人机交互任务。

PhysHOI: 基于物理的动态人 - 物互动模仿

PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction

We devise a 3D scene graph representation, contact graph+ (cg+), for
efficient sequential task planning. Augmented with predicate-like attributes,
this contact graph-based representation abstracts scene layouts with succinct
geometric information and valid robot-scene interactions. Goal configurations,
naturally specified on contact graphs, can be produced by a genetic algorithm
with a stochastic optimization method. A task plan is then initialized by
computing the Graph Editing Distance (GED) between the initial contact graphs
and the goal configurations, which generates graph edit operations
corresponding to possible robot actions. We finalize the task plan by imposing
constraints to regulate the temporal feasibility of graph edit operations,
ensuring valid task and motion correspondences. In a series of simulations and
experiments, robots successfully complete complex sequential object
rearrangement tasks that are difficult to specify using conventional planning
language like Planning Domain Definition Language (PDDL), demonstrating the
high feasibility and potential of robot sequential task planning on contact
graph.

本文提出了一种基于 3D 场景图表示的联系图 +（CG +）的机器人规划方法，利用谓词属性增强这种联系图表示，进行任务规划，计算初始场景图和目标配置之间的图形编辑距离（GED）来初始化任务计划，最终通过实施约束来完成任务，并通过系列实验验证其潜力和可行性。