大规模模拟触觉传感器，揭示机器人灵巧操作中最佳感知配置。
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
- 构建可并行运行的触觉仿真平台，支持多种触觉信号与真实噪声建模。
- 实验证明手部全覆盖比仅指尖覆盖性能提升显著，力/力矩感知最有效。
- 200个触点全手覆盖已足够，为硬件设计和策略选择提供明确指导。
触觉感知对高接触密度的灵巧操作至关重要，但尚不清楚策略需要何种触觉抽象，以及更丰富的触觉场是否值得其硬件成本。这难以通过实验验证：每个传感器相当于一台新机器人，实验室无法复现所有传感器的相同学习实验。本文提出Tactile Genesis，一个基于GPU并行的触觉传感器仿真平台，统一接口下支持二值接触、接触深度、每触点运动学力/力矩、弹性标记位移、几何感知邻近度、接触音频及体素化温度场（机器人学习物理仿真平台首次实现），并支持可配置布局、分辨率及真实噪声模型（漂移、滞后、死触点、串扰）。该平台单卡支持超20,000并行环境与1,000触点，吞吐量较前代提升3至20倍。我们在三个灵巧任务上训练师生策略，系统消融传感器类型、布局、分辨率与噪声，验证了在真实XHand1上的迁移能力。仅本体感觉在所有任务上均不充分；布局优于类型：仅指尖覆盖远落后于全手覆盖，而增加掌部与近端指节可缩小大部分差距；分辨率影响远小于覆盖范围：全手布置200个触点即满足多任务需求。我们发现每触点力/力矩始终是最有用的传感类型。结果为未来触觉硬件设计与灵巧操作中观察选择提供了具体指导。
原文摘要 · Abstract (English)
Tactile sensing is critical for contact-rich dexterous manipulation, yet it remains unclear which tactile abstractions a policy needs and when richer tactile fields justify their hardware cost. This is hard to study empirically: each sensor effectively defines a new robot, and no lab can replicate the same learning experiment across all of them. We present Tactile Genesis, a GPU-parallel tactile sensor simulation platform that exposes binary contact, contact depth, per-taxel kinematic force/torque, elastomer marker displacement, geometry-aware proximity, contact audio, and a voxelized temperature field (the first of its kind in robot learning physics simulation platforms) under a common interface, with configurable placement, resolution, and a realistic noise model (drift, hysteresis, dead taxels, crosstalk). It scales past 20,000 parallel environments and 1,000 taxels on a single GPU, improving throughput by 3 to 20 times over previous tactile simulators. We train teacher-student policies on three dexterous tasks, ablating sensor type, placement, resolution, and noise, and verify transfer to the real XHand1. Proprioception alone is insufficient on every task. Sensor placement dominates sensor type: fingertip-only coverage trails whole-hand coverage by a wide margin, while adding the palm and proximal phalanges closes most of the gap to the privileged teacher. Resolution matters far less than coverage: placing 200 taxels across the whole hand suffices across tasks. We find that force/torque per taxel is consistently the most useful sensor type. These results give concrete guidance for both future tactile hardware design for improving robot hands and policy-side observation choice in dexterous manipulation. https://neuroagents-lab.github.io/tactile-genesis/
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