外观
论文arXiv 2608.158972 个月前
通过瓶颈潜在重构实现无需触觉仿真的触觉 Sim2Real
原文链接：Tactile Sim2Real without Tactile Simulation via Bottlenecked Latent Reconstruction（arXiv 2608.15897 · 发布日期：2026-08-16）
Tactile Sim2Real without Tactile Simulation via Bottlenecked Latent Reconstruction
针对触觉传感器仿真建模困难，SBLR用oracle潜变量与真实传感器对齐，在仿真和硬件任务上验证。
核心要点
- 触觉传感器设计多样且演进迅速，逐个建模需要专业知识，近似计算还可能降低信号保真度。
- SBLR先用易构造的仿真原生oracle传感器训练策略，再将真实传感器潜嵌入对齐到oracle潜空间。
- 策略训练分两阶段：先学习oracle潜变量，再用瓶颈潜在重构适配真实传感器相对oracle的信息损失。
- 仿真三项接触丰富任务中接近或匹配oracle；硬件上零样本成功率85-97.5%，优于物理触觉仿真基线。
验证范围与局限：验证范围包括三个接触丰富的仿真任务，以及使用GelSight Mini和DIGIT传感器的Peg Insertion与Gear Meshing硬件实验。
英文摘要 (Abstract)
Robot sensor designs, particularly tactile sensors, are highly diverse and evolve rapidly. Modeling each sensor in simulation demands substantial domain expertise and computational approximations can degrade the fidelity of the simulated signals. We propose Sim2Real via Bottlenecked Latent Reconstruction (SBLR), a framework that avoids sensor-specific simulation entirely by (1) training policies on a simulator-native oracle sensor that is easy to construct without modeling any particular sensor (e.g. we use a point-cloud and finger-tip forces as a tactile oracle), and (2) aligning real sensor latent embeddings to those of the oracle sensor at inference time. Policy training proceeds in two-stage: the policy first learns from the oracle sensor latents, then a bottlenecked latent reconstruction adapts it to the information loss expected when using the real sensor instead of the oracle. The alignment between oracle and real sensor is learned from unpaired random-play data collected in both simulation and the real world, using rectified-flow-based transformation networks trained on nearest-neighbor pseudo-pairs. Simulation experiments on three contact-rich tasks show that SBLR matches or approaches the performance of an oracle with direct access to tactile simulation. Hardware experiments on Peg Insertion and Gear Meshing with GelSight Mini and DIGIT sensors demonstrate 85-97.5% zero-shot success without requiring any sensor-specific modeling or calibration, outperforming a physics-based tactile simulation baseline by 7.5-15%.
作者：Fan Yang, Youngsun Wi, Jinhao Yu, Nima Fazeli, Dmitry Berenson
来源
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