Benchmarking the Sim-to-Real Gap in Cloth Manipulation

• URL: http://arxiv.org/abs/2310.09543v2
• Date: Thu, 25 Jan 2024 15:35:26 GMT
• Title: Benchmarking the Sim-to-Real Gap in Cloth Manipulation
• Authors: David Blanco-Mulero, Oriol Barbany, Gokhan Alcan, Adri\`a Colom\'e, Carme Torras, Ville Kyrki
• Abstract summary: We present a benchmark dataset to evaluate the sim-to-real gap in cloth manipulation. We use the dataset to evaluate the reality gap, computational time, and stability of four popular deformable object simulators.
• Score: 10.530012817995656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation. By doing so, researchers can circumvent challenges such as sensing the deformation of the object in the realworld. In spite of the extensive use of simulations for this task, few works have evaluated the reality gap between deformable object simulators and real-world data. We present a benchmark dataset to evaluate the sim-to-real gap in cloth manipulation. The dataset is collected by performing a dynamic as well as a quasi-static cloth manipulation task involving contact with a rigid table. We use the dataset to evaluate the reality gap, computational time, and simulation stability of four popular deformable object simulators: MuJoCo, Bullet, Flex, and SOFA. Additionally, we discuss the benefits and drawbacks of each simulator. The benchmark dataset is open-source. Supplementary material, videos, and code, can be found at https://sites.google.com/view/cloth-sim2real-benchmark.

Related papers

• Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications [23.94013806312391]
  We propose a novel approach that dynamically adjusts simulation environment parameters online using in-context learning. We validate our approach across two tasks: object scooping and table air hockey. Our approach delivers efficient and smooth system identification, advancing the deployment of robots in dynamic real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-27T07:13:38Z)
• Reconstructing Objects in-the-wild for Realistic Sensor Simulation [41.55571880832957]
  We present NeuSim, a novel approach that estimates accurate geometry and realistic appearance from sparse in-the-wild data. We model the object appearance with a robust physics-inspired reflectance representation effective for in-the-wild data. Our experiments show that NeuSim has strong view synthesis performance on challenging scenarios with sparse training views.
  arXiv  Detail & Related papers  (2023-11-09T18:58:22Z)
• Learning Interactive Real-World Simulators [96.5991333400566]
  We explore the possibility of learning a universal simulator of real-world interaction through generative modeling. We use the simulator to train both high-level vision-language policies and low-level reinforcement learning policies. Video captioning models can benefit from training with simulated experience, opening up even wider applications.
  arXiv  Detail & Related papers  (2023-10-09T19:42:22Z)
• Robust Visual Sim-to-Real Transfer for Robotic Manipulation [79.66851068682779]
  Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR)
  arXiv  Detail & Related papers  (2023-07-28T05:47:24Z)
• Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation [4.7246285569677315]
  We present Sim-to-Real Dense Object Nets (SRDONs), a dense object descriptor that not only understands the object via appropriate representation but also maps simulated and real data to a unified feature space with pixel consistency. We demonstrate in experiments that pre-trained SRDONs significantly improve performances on unseen objects and unseen visual environments for various robotic tasks with zero real-world training.
  arXiv  Detail & Related papers  (2023-04-18T02:28:55Z)
• Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level [1.1999555634662635]
  LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. We show that using simulated data for the training of neural networks leads to a domain shift of training and testing data due to differences in scenes, scenarios, and distributions.
  arXiv  Detail & Related papers  (2023-03-03T12:52:01Z)
• Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly [55.41644538483948]
  We present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data. A novel patch-based attention network is proposed additionally to tackle this problem.
  arXiv  Detail & Related papers  (2023-01-12T14:00:37Z)
• 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)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition [81.24107630746508]
  We present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes. We first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. The resulting synthetic images are photorealistic, traffic-aware, and geometrically consistent, allowing image simulation to scale to complex use cases.
  arXiv  Detail & Related papers  (2021-01-16T23:00:33Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

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.