ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots

TL;DR

This paper introduces a foundation model for general-purpose robotic manipulation that formalizes contact synthesis, enabling robots to manipulate diverse objects with high success rates in simulation and real-world settings.

Contribution

The work presents a comprehensive framework for a foundation model that formalizes manipulation as contact synthesis, handling various objects and robots in a unified manner.

Findings

Achieves around 90% success rate in manipulation tasks

Handles diverse objects including articulated, rigid, and deformable

Validated in both simulation and real-world environments

Abstract

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/.