AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation (2407.07889v1)

Abstract: Predictive models are a crucial component of many robotic systems. Yet, constructing accurate predictive models for a variety of deformable objects, especially those with unknown physical properties, remains a significant challenge. This paper introduces AdaptiGraph, a learning-based dynamics modeling approach that enables robots to predict, adapt to, and control a wide array of challenging deformable materials with unknown physical properties. AdaptiGraph leverages the highly flexible graph-based neural dynamics (GBND) framework, which represents material bits as particles and employs a graph neural network (GNN) to predict particle motion. Its key innovation is a unified physical property-conditioned GBND model capable of predicting the motions of diverse materials with varying physical properties without retraining. Upon encountering new materials during online deployment, AdaptiGraph utilizes a physical property optimization process for a few-shot adaptation of the model, enhancing its fit to the observed interaction data. The adapted models can precisely simulate the dynamics and predict the motion of various deformable materials, such as ropes, granular media, rigid boxes, and cloth, while adapting to different physical properties, including stiffness, granular size, and center of pressure. On prediction and manipulation tasks involving a diverse set of real-world deformable objects, our method exhibits superior prediction accuracy and task proficiency over non-material-conditioned and non-adaptive models. The project page is available at https://robopil.github.io/adaptigraph/ .

• The paper presents a unified graph-based neural dynamics model that adapts to varying material properties for robotic manipulation.
• It uses a few-shot adaptation procedure and particle representation to refine predictions in real time without retraining.
• Experimental results show superior performance in tasks such as rope straightening, granular gathering, and cloth relocating.

AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation

The presented paper introduces AdaptiGraph, an advanced graph-based neural dynamics model tailored for manipulating a wide range of deformable materials with unknown physical properties. AdaptiGraph overcomes the inherent challenges in predictive modeling for robotic systems, specifically focusing on objects constructed from different materials, including ropes, granular media, rigid boxes, and cloths. This versatility is achieved by conditioning the predictive model on physical property variables, enabling robust dynamics simulation and effective manipulation even under varying physical properties.

Key Innovations and Methodology

The foundational element of AdaptiGraph is its utilization of a highly flexible Graph-Based Neural Dynamics (GBND) framework. This approach represents aspects of materials as particles and leverages a Graph Neural Network (GNN) to predict the motion of these particles. The key innovation lies in its unified, continuous variable-conditioned GBND model which does not necessitate retraining when applied to materials with differing physical properties. This marks a significant shift from prior approaches that typically require extensive reconfiguration to handle new material properties.

To implement this approach, the authors propose a few-shot adaptation procedure that employs a physical property optimization process during online deployment. Upon encountering new, unseen materials, AdaptiGraph iteratively refines its predictive model in near real-time, optimizing its fit based on the observed interaction data. This mechanism substantially enhances the model's ability to adapt to and accurately simulate the dynamic behaviors of diverse materials.

Experimental Validation

The effectiveness of AdaptiGraph is substantiated through rigorous experiments spanning four distinct material categories:

1. Rigid Box Pushing: Optimizing robot actions to push boxes to specific positions and orientations.
2. Rope Straightening: Rearranging ropes into target configurations, accounting for variations in stiffness.
3. Granular Pile Gathering: Collecting granular materials into defined regions, considering different granular sizes.
4. Cloth Relocating: Precisely manipulating cloths to target placements on surfaces, differentiating by stiffness.

The framework's prediction accuracy and task competency were validated against non-material-conditioned and non-adaptive baseline models. AdaptiGraph consistently demonstrated superior performance, particularly noted in its precise simulation of object dynamics and effective manipulation in practical tasks.

Implications and Future Developments

The practical implications of AdaptiGraph are substantial. It equips robotic systems with the ability to handle materials of various types and properties without the necessity for extensive retraining, thereby markedly enhancing the versatility and efficiency of robotic operations in dynamic environments. This advancement is particularly significant for applications in manufacturing, automation, and potentially healthcare, where varying material properties are common.

On a theoretical level, the paper proposes a novel approach to integrating continuous variable conditioning in a GNN framework, paving the way for further research in multi-material and heterogeneous object interactions. Future developments could extend this methodology to incorporate more materials and a broader spectrum of physical properties, thus widening the applications. Moreover, the adaptation mechanism's potential improvements could explore more sophisticated optimization techniques, such as uncertainty-aware adaptations to further enhance model robustness and accuracy.

AdaptiGraph represents a noteworthy progression in the modeling and manipulation of deformable materials in robotics, promising to augment both the theoretical framework and practical capabilities of robotic systems in handling diverse, dynamic environments. The methodologies and insights from this work will likely serve as a foundation for further advancements in adaptive robotic manipulation and autonomous system interactions.