ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots (2405.06964v2)

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/.

ManiFoundation Model for General-Purpose Robotic Manipulation

The paper "ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots" presents a robust, comprehensive framework dedicated to advancing robotic intelligence. The proposed foundation model is designed to empower robots to proficiently execute an extensive array of manipulation tasks involving diverse objects and robotic configurations. This capability is akin to the versatile task-planning abilities observed in LLMs.

Framework Overview

The framework introduces a novel method of framing a manipulation task as one of contact synthesis. It comprises an input profile incorporating the object and robot manipulator point clouds, objects' physical attributes, desired motion targets, and manipulation region masks. The output produced by the model includes the contact points on the object and the associated contact forces or post-contact motions that are necessary for accomplishing the intended manipulation task. The intent is to equip robots with the ability to interact with articulated rigid objects, rigid materials, and deformable items, ranging from ropes to more complex 3D forms like plasticine.

Experimental Validation and Results

An extensive set of experiments, conducted both in simulated environments and real-world settings, tested the model across varying object types and conditions. The model demonstrated an impressive average success rate of approximately 90%. This validation underscores the model's efficacy in adapting to different types of objects and manipulation scenarios.

Theoretical and Practical Implications

The research provides significant contributions to both theoretical paradigms and practical applications of robotic manipulation. Theoretically, the framework establishes a universal approach to task formulation, applicable to an array of robotic tasks, similar to autoregressive prediction tasks in models like GPT and mask prediction in BERT. This universality could lead to broader adaptability and integration across different robotic platforms. Practically, this model could enable improvements in industrial automation, service robots, and any domain where robots are required to interact with diverse and unpredictable material environments.

Future Prospects

Looking forward, the research opens several avenues for further exploration. Future work could extend the model's framework to support high-dynamic manipulation tasks, possibly by considering multi-step interactions or entire trajectories at each time step. Additionally, the model could be expanded to address more complex interaction scenarios, such as those involving multiple hands or surface contacts rather than point contacts.

Conclusion

In conclusion, the "ManiFoundation Model for General-Purpose Robotic Manipulation" represents a significant step forward in the development of flexible, adaptable robotic systems. By effectively synthesizing contact strategies through a well-designed neural network and iterative optimization, this framework lays the ground for more intelligent and capable robots, showing strong promise for versatile applications in robotics manipulation.