Recent years have seen significant advancements in humanoid control, largely
due to the availability of large-scale motion capture data and the application
of reinforcement learning methodologies. However, many real-world tasks, such
as moving large and heavy furniture, require multi-character collaboration.
Given the scarcity of data on multi-character collaboration and the efficiency
challenges associated with multi-agent learning, these tasks cannot be
straightforwardly addressed using training paradigms designed for single-agent
scenarios. In this paper, we introduce Cooperative Human-Object Interaction
(CooHOI), a novel framework that addresses multi-character objects transporting
through a two-phase learning paradigm: individual skill acquisition and
subsequent transfer. Initially, a single agent learns to perform tasks using
the Adversarial Motion Priors (AMP) framework. Following this, the agent learns
to collaborate with others by considering the shared dynamics of the
manipulated object during parallel training using Multi Agent Proximal Policy
Optimization (MAPPO). When one agent interacts with the object, resulting in
specific object dynamics changes, the other agents learn to respond
appropriately, thereby achieving implicit communication and coordination
between teammates. Unlike previous approaches that relied on tracking-based
methods for multi-character HOI, CooHOI is inherently efficient, does not
depend on motion capture data of multi-character interactions, and can be
seamlessly extended to include more participants and a wide range of object
types

通过合作人物 - 目标交互（CooHOI）框架，通过两阶段学习范式：个体技能获取和随后的迁移，解决多人物物体运输问题，实现团队成员之间的隐式沟通和协调。

CooHOI: 学习通过操纵对象动力学的协作人 - 物互动

CooHOI: Learning Cooperative Human-Object Interaction with Manipulated  Object Dynamics

Humanoid control is an important research challenge offering avenues for
integration into human-centric infrastructures and enabling physics-driven
humanoid animations. The daunting challenges in this field stem from the
difficulty of optimizing in high-dimensional action spaces and the instability
introduced by the bipedal morphology of humanoids. However, the extensive
collection of human motion-captured data and the derived datasets of humanoid
trajectories, such as MoCapAct, paves the way to tackle these challenges. In
this context, we present Humanoid Generalist Autoencoding Planner (H-GAP), a
state-action trajectory generative model trained on humanoid trajectories
derived from human motion-captured data, capable of adeptly handling downstream
control tasks with Model Predictive Control (MPC). For 56 degrees of freedom
humanoid, we empirically demonstrate that H-GAP learns to represent and
generate a wide range of motor behaviours. Further, without any learning from
online interactions, it can also flexibly transfer these behaviors to solve
novel downstream control tasks via planning. Notably, H-GAP excels established
MPC baselines that have access to the ground truth dynamics model, and is
superior or comparable to offline RL methods trained for individual tasks.
Finally, we do a series of empirical studies on the scaling properties of
H-GAP, showing the potential for performance gains via additional data but not
computing. Code and videos are available at
this https URL

人型控制是一个重要的研究挑战，为人类中心基础设施的整合和物理驱动的人型动画提供了可能。我们提出了一种名为 H-GAP 的人型通用自编码规划器，它是基于人型轨迹数据训练得到的模型，能够通过模型预测控制（MPC）熟练地处理下游控制任务。H-GAP 不仅在处理任务方面表现出色，而且在性能上超过了具备地面实况模型的 MPC 基准线，并且与针对各个任务训练的离线强化学习方法相当或优越。

H-GAP：具备广义规划器的人形控制

H-GAP: Humanoid Control with a Generalist Planner

We present a universal motion representation that encompasses a comprehensive
range of motor skills for physics-based humanoid control. Due to the
high-dimensionality of humanoid control as well as the inherent difficulties in
reinforcement learning, prior methods have focused on learning skill embeddings
for a narrow range of movement styles (e.g. locomotion, game characters) from
specialized motion datasets. This limited scope hampers its applicability in
complex tasks. Our work closes this gap, significantly increasing the coverage
of motion representation space. To achieve this, we first learn a motion
imitator that can imitate all of human motion from a large, unstructured motion
dataset. We then create our motion representation by distilling skills directly
from the imitator. This is achieved using an encoder-decoder structure with a
variational information bottleneck. Additionally, we jointly learn a prior
conditioned on proprioception (humanoid's own pose and velocities) to improve
model expressiveness and sampling efficiency for downstream tasks. Sampling
from the prior, we can generate long, stable, and diverse human motions. Using
this latent space for hierarchical RL, we show that our policies solve tasks
using natural and realistic human behavior. We demonstrate the effectiveness of
our motion representation by solving generative tasks (e.g. strike, terrain
traversal) and motion tracking using VR controllers.

我们提出了一种通用的运动表示方法，该方法涵盖了针对基于物理的人形控制的全面的运动技能范围。