DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting (2203.10263v1)

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. Next, we illustrate that control parameters in the simulation can be optimized to minimize cutting forces via lateral slicing motions. Finally, we conduct experiments on a real robot arm equipped with a slicing knife to infer simulation parameters from force measurements. By optimizing the slicing motion of the knife, we show on fruit cutting scenarios that the average knife force can be reduced by more than 40% compared to a vertical cutting motion. We publish code and additional materials on our project website at https://diff-cutting-sim.github.io.

• The paper introduces DiSECt, the first differentiable simulator for robotic cutting of soft materials, enabling efficient gradient-based parameter inference and control optimization.
• DiSECt demonstrates efficient, gradient-based parameter inference over hundreds of dimensions and validates its predictions against real-world data for robust sim2real transfer.
• The simulator enables optimization of robotic cutting control strategies to minimize forces and generalizes across varying conditions, showing promise for applications like medical robotics.

Analysis of "DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting"

The paper presents DiSECt, the inaugural differentiable simulator focused on the intricacies of cutting soft materials, tailored specifically for robotics applications. The core advancement here lies in the integration of a differentiable simulation framework, which enables efficient parameter inference and control strategy optimization through gradient-based methods.

Robotic cutting of soft materials is pivotal in domains like food processing, household automation, surgical interventions, and more. The inherent destructiveness and complexity of cutting, involving phenomena like elastic and plastic deformations, friction, and fracture, present significant challenges to traditional simulators. DiSECt advances beyond these challenges by leveraging differentiability to provide a simulator that can not only replicate cutting dynamics but also adjust its parameters to fit empirical data with high fidelity.

Scientific Contributions and Methodologies

1. Differentiable Simulation Framework: DiSECt employs a novel combination of FEM with a continuous contact model using signed distance fields and a damage model. The damage model facilitates crack formation by weakening springs inserted along a cutting plane until they yield. This integration of differentiability is a significant milestone, as it allows the use of gradient-based optimization techniques to tune simulation parameters, directly linking theoretical physics with empirical parameter estimation.
2. Efficiency in Parameter Inference: Traditional parameter estimation often relies on derivative-free methods, which are typically computationally expensive and less efficient. The differentiable nature of DiSECt allows the paper to demonstrate the efficiency of gradient-based methods (like stochastic gradient Langevin dynamics) in inferring simulation parameters over hundreds of dimensions while maintaining remarkable speed and accuracy.
3. Real-World Validation and Sim2Real Transfer: Through extensive experimental validation, the simulator's predictions show strong correlation with both a state-of-the-art commercial simulator and real-world data. The interface and integration with Bayesian inference facilitate robust uncertainty quantification, thus endowing DiSECt with versatile applicability across different cutting velocities and object geometries.
4. Control Optimization: The paper articulately demonstrates the potential of DiSECt for optimizing control strategies, where optimized trajectories are found to minimize forces involved in the cutting process. This reduction in force application confirms the practical implications of the simulator, potentially minimizing damage to soft materials and optimizing cutting efficiency.
5. Generative Capability: Importantly, the paper provides evidence for the generalization capabilities of the simulator. Parameters tuned for one set of conditions displayed robustness when applied across different velocities and geometrical scenarios, which is crucial for deploying such simulators in dynamically changing environments.

Implications and Future Developments

This work not only bridges a notable gap in the current repertoire of robotics simulations but also sets a precedent for future endeavors in creating highly adaptive and accurate simulation tools. The introduction of a differentiable simulator could encourage deeper integration with machine learning paradigms, particularly for tasks that necessitate online adaptation and learning.

For medical robotics, such as surgical automation, the adaptive nature of DiSECt could significantly enhance safety and precision. The simulator's capability to infer precise material properties in situ could lead to real-time adjustments in surgical plans and potentially automate complex tasks with minimal oversight, provided reliability and calibration mechanisms are continually refined.

To capitalize on the promising results of DiSECt, future research could explore:

• Expanding the range of sliceable objects beyond biomaterials, incorporating anisotropic and nonlinear material properties.
• Integrating with reinforcement learning paradigms for end-to-end training of robotic systems using simulated environments.
• Developing a standardized set of benchmarks to objectively measure the performance of differentiable simulators against traditional, non-differentiable counterparts.

By embracing the directions outlined herein, researchers and practitioners can further harness differentiable simulation as both a research tool and a commercial application, ultimately driving innovation in the field of robotic manipulation and soft material interaction.