FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes

• URL: http://arxiv.org/abs/2604.17513v1
• Date: Sun, 19 Apr 2026 16:07:43 GMT
• Title: FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes
• Abstract summary: FLASH is a GPU-native simulation framework for contact-rich deformable manipulation.<n>It is built on an accurate NCP-based solver that enforces strict contact and deformation constraints.
• Score: 15.151803709340404
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulation remains a major bottleneck for deformable object manipulation. The continuously changing geometry of soft materials, together with large numbers of vertices and contact constraints, makes it difficult to achieve high accuracy, speed, and stability required for large-scale interactive learning. We present FLASH, a GPU-native simulation framework for contact-rich deformable manipulation, built on an accurate NCP-based solver that enforces strict contact and deformation constraints while being explicitly designed for fine-grained GPU parallelism. Rather than porting conventional single-instruction-multiple-data (SIMD) solvers to GPUs, FLASH redesigns the physics engine from the ground up to leverage modern GPU architectures, including optimized collision handling and memory layouts. As a result, FLASH scales to over 3 million degrees of freedom at 30 FPS on a single RTX 5090, while accurately simulating physical interactions. Policies trained solely on FLASH-generated synthetic data in minutes achieve robust zero-shot sim-to-real transfer, which we validate on physical robots performing challenging deformable manipulation tasks such as towel folding and garment folding, without any real-world demonstration, providing a practical alternative to labor-intensive real-world data collection.

Related papers

• GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning [55.272458304179025]
  GS-Playground is a multi-modal simulation framework designed to accelerate end-to-end perceptual learning.<n>Our system achieves a breakthrough throughput of 104 FPS at 640x480 resolution, significantly lowering the barrier for large-scale visual RL.
  arXiv  Detail & Related papers  (2026-04-28T10:05:39Z)
• SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds [36.87909899472009]
  We introduce SIM1, a physics-aligned real-to-sim-to-real data engine that grounds simulation in the physical world.<n>The system digitizes scenes into metric-consistent twins, calibrates deformable dynamics through elastic modeling, and expands behaviors via diffusion-based trajectory generation.<n>Experiments show that policies trained on purely synthetic data achieve parity with real-data baselines at a 1:15 equivalence ratio, while delivering 90% zero-shot success and 50% generalization gains in real-world deployment.
  arXiv  Detail & Related papers  (2026-04-09T17:59:52Z)
• ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control [7.095015036485825]
  We present a GPU-parallelized analytical contact physics engine built on complementarity-free contact modeling.<n>ComFree-Sim computes contact impulses in closed form via an impedance-style prediction--correction update in the dual cone of Coulomb friction.
  arXiv  Detail & Related papers  (2026-03-12T17:14:45Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer [62.18680935878919]
  We introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings into temporally consistent outputs.<n>At its core is a single-step temporally-conditioned enhancer capable of running in online simulators on a single GPU.
  arXiv  Detail & Related papers  (2026-02-27T15:35:30Z)
• ExoGS: A 4D Real-to-Sim-to-Real Framework for Scalable Manipulation Data Collection [27.18540416686717]
  ExoGS is a robot-free 4D Real-to-Sim-to-Real framework.<n>It captures both static environments and dynamic interactions in the real world and transfers them seamlessly to a simulated environment.<n>It provides a new solution for scalable manipulation data collection and policy learning.
  arXiv  Detail & Related papers  (2026-01-26T16:04:12Z)
• SORS: A Modular, High-Fidelity Simulator for Soft Robots [5.660024841218955]
  Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions.<n>We present SORS, a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications.
  arXiv  Detail & Related papers  (2025-12-17T21:58:46Z)
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning [72.43357471969564]
  Isaac Lab combines high-fidelity GPU parallel physics, rendering, and a modular, composable architecture for designing environments and training robot policies.<n>We highlight its application to a diverse set of challenges, including whole-body control, cross-embodiment mobility, contact-rich and dexterous manipulation, and the integration of human demonstrations for skill acquisition.<n>We believe Isaac Lab's combination of advanced simulation capabilities, rich sensing, and data-center scale execution will help unlock the next generation of breakthroughs in robotics research.
  arXiv  Detail & Related papers  (2025-11-06T21:43:02Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• 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)

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.