Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy (2505.17434v1)

Abstract: Goal-conditioned dynamic manipulation is inherently challenging due to complex system dynamics and stringent task constraints, particularly in deformable object scenarios characterized by high degrees of freedom and underactuation. Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks. In this work, we explore 3D goal-conditioned rope manipulation as a representative challenge. To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics, which enables compact state representation and facilitates efficient policy learning. Building on this, we propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation. First, we design a diffusion policy that learns inverse dynamics within the reduced-order space, enabling imitation learning to move beyond na\"ive data fitting and capture the underlying physical structure. Second, we propose a physics-informed test-time adaptation scheme that imposes kinematic boundary conditions and structured dynamics priors on the diffusion process, ensuring consistency and reliability in manipulation execution. Extensive experiments validate the proposed approach, demonstrating strong performance in terms of accuracy and robustness in the learned policy.

Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark, and Learning Strategy

"Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy" provides a comprehensive exploration into the field of robotics, particularly focusing on the manipulation of deformable objects which involve intricate 3D dynamic environments. This paper elucidates the challenges associated with goal-conditioned dynamic manipulation tasks, specifically highlighting the complexities derived from high-dimensional dynamics and underactuation in systems handling deformable materials.

Key Contributions

1. Simulation Framework and Benchmark Creation: The paper introduces a novel simulation framework grounded in reduced-order dynamics, enabling the manipulation of deformable objects, such as ropes, in a 3D space. This approach significantly reduces the dimensional complexity compared to traditional methods like Finite Element Methods (FEM), thereby facilitating more efficient policy learning and data simulation.
2. Dynamics-Informed Diffusion Policy (DIDP): A pivotal contribution of the paper is the DIDP, a diffusion-based policy framework integrating imitation pretraining with physics-informed adaptation during test-time. DIDP aims to capture the inverse dynamics within reduced-order space, surpassing traditional data-fitting approaches by embedding kinematic boundaries and structured dynamics priors into its policy learning process.
3. Reduced-Order GVS Model Use: The adaptation of the Geometric Variable Strain (GVS) model allows for the creation of a simulation environment with only 20 degrees of freedom (DoF), optimizing computational efficiency and enabling the modeling of both rigid and deformable components in a unified, differentiable dynamics space.

Experimentation and Results

The paper outlines a detailed experimental setup where the DIDP is evaluated against various benchmarks, focusing on dynamic rope manipulation to hit specified goal locations with high precision. The extensive results demonstrate the robust performance of DIDP in terms of accuracy and efficiency. For instance, when utilizing the task of rope whipping, metrics such as Euclidean distance to goal positions were employed to measure success rates at variable thresholds (1 cm, 2 cm, 5 cm, and 10 cm). The DIDP exhibited a substantial increase in performance compared to baseline methods, showcasing its capability to learn both accurate and generalizable manipulation strategies from sparse datasets.

Implications and Future Directions

The implications of this research are multidimensional, especially in advancing the field of robotic manipulation of soft materials. From a practical standpoint, the reduced computational overhead and improved learning efficiency facilitated by DIDP could enhance real-world robotic applications in diverse domains such as manufacturing, healthcare, and service industries. Theoretically, the integration of reduced-order dynamic models and diffusion-based learning opens new avenues for research in high-dimensional control systems.

Future developments in AI derived from this research could involve expanding the dataset to encompass a wider variety of deformable objects with different material properties. Additionally, exploring extensions to the DIDP that enhance multi-task learning capabilities could significantly broaden its applicability.

In conclusion, this paper provides a structured approach to overcoming inherent challenges in the dynamic manipulation of deformable objects, offering promising directions for future research and applications in AI-driven robotics.