  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
