DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting (2105.12244v1)

Abstract: Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators can facilitate controller verification, policy learning, and dataset generation. Moreover, differentiable simulators can enable gradient-based optimization, which is invaluable for calibrating simulation parameters and optimizing controllers. In this work, we present DiSECt: the first differentiable simulator for cutting soft materials. The simulator augments the finite element method (FEM) with a continuous contact model based on signed distance fields (SDF), as well as a continuous damage model that inserts springs on opposite sides of the cutting plane and allows them to weaken until zero stiffness, enabling crack formation. Through various experiments, we evaluate the performance of the simulator. We first show that the simulator can be calibrated to match resultant forces and deformation fields from a state-of-the-art commercial solver and real-world cutting datasets, with generality across cutting velocities and object instances. We then show that Bayesian inference can be performed efficiently by leveraging the differentiability of the simulator, estimating posteriors over hundreds of parameters in a fraction of the time of derivative-free methods. Finally, we illustrate that control parameters in the simulation can be optimized to minimize cutting forces via lateral slicing motions. We publish videos and additional results on our project website at https://diff-cutting-sim.github.io.

• The paper introduces the first differentiable simulator for soft material cutting using FEM and SDF-based contact models.
• The methodology employs continuous damage modeling with virtual springs to emulate crack formation and optimize control strategies.
• The engine achieves efficient Bayesian parameter inference and calibration, matching results with commercial solvers and real-world data.

Overview of DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

The paper "DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting" introduces a novel approach to simulating the act of robotic cutting of soft materials. This is particularly crucial for applications such as food processing, surgical procedures, and various forms of automation where accurate predictions of cutting dynamics are essential. DiSECt represents a significant development by being the first differentiable simulator specifically designed for cutting soft materials, enabling efficient gradient-based optimization for simulation calibration and control parameter optimization.

Simulation Design and Methodologies

The core innovation in DiSECt lies in its ability to simulate the complex physical interactions involved in cutting using the finite element method (FEM) coupled with a continuous contact model and a damage model based on signed distance fields (SDF) for the knife. Key features of this simulator include:

• Continuous Contact Model: The interaction between the knife and the object is managed via an SDF representation, allowing for accurate calculation of contact forces and gradients.
• Damage Model: The simulator introduces a continuous damage model where virtual spring elements are inserted along potential fracture lines. These springs weaken over time and stress until a complete fracture occurs, enabling an accurate simulation of crack formation.
• Differentiability: The use of automatic differentiation allows for obtaining gradients with respect to simulation parameters, which is crucial for both parameter inference and controller optimization.

The simulator is capable of being calibrated to match real-world data and high-fidelity commercial solvers, demonstrating generality across different velocities and material types.

Numerical Results and Performance Evaluation

DiSECt shows strong numerical performance in several key areas:

• Efficiency in Bayesian Inference: By leveraging differentiability, DiSECt estimates posterior distributions over hundreds of parameters efficiently compared to traditional derivative-free methods.
• Calibration Against Commercial Solvers: The simulator matches well with the results from a commercial solver and real-world datasets, with prediction speeds being significantly faster.
• Control Optimization: Through optimization, cutting strategies that minimize the required force can be deduced, which is particularly beneficial in contexts like robotic surgery.

Implications and Future Work

Practically, DiSECt opens up new possibilities for more reliable and rapid simulations in robotic cutting applications. The ability to optimize control strategies in the simulator can lead to more efficient and precise robotic operations, reducing material waste and increasing safety in applications like surgical robotics.

Theoretically, DiSECt contributes to the field of differentiable simulations by showcasing the benefits of integrating automatic differentiation into complex physical processes like cutting. This is likely to influence future developments in simulation technology not just in the context of cutting but potentially other destructively interacting systems.

Future research directions suggested by the authors include enhancing the material models to capture non-linear and anisotropic behavior more accurately, which is essential for simulating biological tissues. Additionally, extending the simulator to interactively accommodate real-time cutting adjustments and evaluating online surgical planning strategies are prospective areas for exploration.

In summary, DiSECt represents a significant step forward in simulating complex interactions in robotic systems, offering both theoretical and practical advancements in the field of robotics and automated systems. Its differentiability and comprehensive model make it a powerful tool for optimizing cutting tasks in various domains.