  To substantially enhance robot intelligence, there is a pressing need to
develop a large model that enables general-purpose robots to proficiently
undertake a broad spectrum of manipulation tasks, akin to the versatile
task-planning ability exhibited by LLMs. The vast diversity in objects, robots,
and manipulation tasks presents huge challenges. Our work introduces a
comprehensive framework to develop a foundation model for general robotic
manipulation that formalizes a manipulation task as contact synthesis.
Specifically, our model takes as input object and robot manipulator point
clouds, object physical attributes, target motions, and manipulation region
masks. It outputs contact points on the object and associated contact forces or
post-contact motions for robots to achieve the desired manipulation task. We
perform extensive experiments both in the simulation and real-world settings,
manipulating articulated rigid objects, rigid objects, and deformable objects
that vary in dimensionality, ranging from one-dimensional objects like ropes to
two-dimensional objects like cloth and extending to three-dimensional objects
such as plasticine. Our model achieves average success rates of around 90\%.
Supplementary materials and videos are available on our project website at
https://manifoundationmodel.github.io/.
