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

• URL: http://arxiv.org/abs/2203.10263v1
• Date: Sat, 19 Mar 2022 07:27:19 GMT
• Title: DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
• Authors: Eric Heiden, Miles Macklin, Yashraj Narang, Dieter Fox, Animesh Garg, Fabio Ramos
• Abstract summary: We present DiSECt: the first differentiable simulator for cutting soft materials. The simulator augments the finite element method with a continuous contact model based on signed distance fields. We show that the simulator can be calibrated to match resultant forces and fields from a state-of-the-art commercial solver.
• Score: 71.50844437057555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Learning Quadruped Locomotion Using Differentiable Simulation [31.80380408663424]
  Differentiable simulation promises fast convergence and stable training. This work proposes a new differentiable simulation framework to overcome these challenges. Our framework enables learning quadruped walking in simulation in minutes without parallelization.
  arXiv  Detail & Related papers  (2024-03-21T22:18:59Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects [18.266002992029716]
  Research in manipulation of deformable objects is typically conducted on a limited range of scenarios. Realistic simulators with support for various types of deformations and interactions have the potential to speed up experimentation. For highly deformable objects it is challenging to align the output of a simulator with the behavior of real objects.
  arXiv  Detail & Related papers  (2022-04-07T00:45:26Z)
• DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools [96.38972082580294]
  DiffSkill is a novel framework that uses a differentiable physics simulator for skill abstraction to solve deformable object manipulation tasks. In particular, we first obtain short-horizon skills using individual tools from a gradient-based simulator. We then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input.
  arXiv  Detail & Related papers  (2022-03-31T17:59:38Z)
• Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators [21.349079159359746]
  Soft actuators offer a safe and adaptable approach to robotic tasks like gentle grasping and dexterous movement. Creating accurate models to control such systems is challenging due to the complex physics of deformable materials. This paper presents a framework that combines the advantages of differentiable simulator and Finite Element Method.
  arXiv  Detail & Related papers  (2022-02-25T21:15:05Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
  We introduce a novel high-quality neural simulator referred to as DriveGAN. DriveGAN achieves controllability by disentangling different components without supervision. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
  arXiv  Detail & Related papers  (2021-04-30T15:30:05Z)
• Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs [15.208516853395453]
  Most high-fidelity robotic simulators like ours are time-consuming. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved.
  arXiv  Detail & Related papers  (2021-03-13T06:22:23Z)
• Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery [13.657060682152409]
  We investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. We use an open-source da Vinci Surgical System to probe a soft-tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud.
  arXiv  Detail & Related papers  (2020-03-14T00:16:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.