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TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators

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arxiv 2411.04776 v1 pith:2RPQKOAI submitted 2024-11-07 cs.RO

Duc Huy Nguyen , Tim Schneider , Guillaume Duret , Alap Kshirsagar , Boris Belousov , Jan Peters This is my paper · ORCID
classification cs.RO
keywords simulationtacextactilegelsightisaacsimulatorsimulatorssoft-body
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Training robot policies in simulation is becoming increasingly popular; nevertheless, a precise, reliable, and easy-to-use tactile simulator for contact-rich manipulation tasks is still missing. To close this gap, we develop TacEx -- a modular tactile simulation framework. We embed a state-of-the-art soft-body simulator for contacts named GIPC and vision-based tactile simulators Taxim and FOTS into Isaac Sim to achieve robust and plausible simulation of the visuotactile sensor GelSight Mini. We implement several Isaac Lab environments for Reinforcement Learning (RL) leveraging our TacEx simulation, including object pushing, lifting, and pole balancing. We validate that the simulation is stable and that the high-dimensional observations, such as the gel deformation and the RGB images from the GelSight camera, can be used for training. The code, videos, and additional results will be released online https://sites.google.com/view/tacex.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Success-only metrics overstate deformable-manipulation performance; tactile sensing raises Safety Success (e.g. 21.4%→35.6% on Object-Soft) while Goal Success stays comparable.

  2. Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation

    cs.RO 2025-12 unverdicted novelty 6.0 of 10

    DreamTacVLA grounds VLA models in contact physics by aligning multi-scale vision-tactile inputs and predicting future tactile states, reaching up to 95% success on contact-rich tasks.

  3. ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

    cs.RO 2026-07 conditional novelty 5.0 of 10

    An action-conditioned visuo-tactile world model generates synthetic camera-plus-touch rollouts that, mixed with real demonstrations, improve downstream contact-rich manipulation policies.

  4. Data Pyramid for Embodied Manipulation

    cs.RO 2026-07 conditional novelty 3.0 of 10

    Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.

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