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Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation

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arxiv 2504.12908 v2 pith:IPQXCX3G submitted 2025-04-17 cs.RO cs.CV

classification cs.ROcs.CV
keywords tactilesimulationroboticstaccelachievingcapabilitieshigh-performancelimited
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Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. As a promising solution, Vision-Based Tactile Sensors (VBTSs) offer high spatial resolution and cost-effectiveness, but present unique challenges in robotics for their complex physical characteristics and visual signal processing requirements. The lack of efficient and accurate simulation tools for VBTSs has significantly limited the scale and scope of tactile robotics research. We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed, achieving an 18-fold acceleration over real-time across thousands of parallel environments. Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs. Through extensive validation in object recognition, robotic grasping, and articulated object manipulation, we demonstrate precise simulation and successful sim-to-real transfer. These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.

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Cited by 2 Pith papers

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

  1. Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks

    cs.RO 2026-06 unverdicted novelty 7.0 of 10

    Whole-hand tactile coverage and per-taxel force/torque dominate sensor type and resolution for learning three dexterous tasks in a new high-throughput tactile simulator.

  2. Data Pyramid for Embodied Manipulation: A Survey

    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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