Source note
ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation
Summary
ETac is a tactile simulation framework for dexterous manipulation that aims to keep FEM-like deformation quality without FEM-level cost. It combines a simple physics prior with a small learned correction model, then uses the simulated tactile field to train blind grasping policies at large RL scale.
Problem
- Tactile RL needs many simulated interactions, but high-fidelity soft-body simulators such as FEM are too slow for large parallel training.
- Faster tactile simulators often only model local indentation and miss deformation propagation across the elastomer, which hurts realism, especially on curved sensor surfaces.
- This matters because modern tactile hands depend on rich contact patterns, not just binary contact or force signals, for stable dexterous manipulation.
Approach
- ETac discretizes the tactile sensor surface into 3D nodes and detects contact with signed distance fields. Nodes in contact are "active" nodes.
- It estimates deformation of non-contacted "passive" nodes with two parts: an exponential distance-decay propagation term and a lightweight residual network.
- The decay term gives a fast physical prior for how indentation spreads across the elastomer surface.
- The residual network, built with a PointNet-style encoder and MLP decoder, corrects errors from the linear decay model and captures nonlinear effects such as curvature, anisotropy, and interaction between contact points.
- The propagation parameters are trained to match FEM-generated deformation data, then the resulting displacement field is used as tactile input for PPO on a blind grasping task with a ShadowHand.
Results
- On deformation estimation against FEM ground truth, ETac reports the best RMSE among lightweight baselines: 0.058 ± 0.034 mm on a flat elastomer and 0.116 ± 0.049 mm on a curved elastomer, versus TacSL: 0.194 / 0.445 mm and Taxim: 0.163 / 0.447 mm.
- The full model beats its own ablations: linear-only gets 0.151 / 0.256 mm RMSE and residual-only gets 0.074 / 0.128 mm on flat/curved elastomers.
- For real sensor response prediction, data generated with ETac gives 3.94% L1 loss on a flat sensor and 3.61% on a curved sensor, compared with 2.46% and 2.75% using FEM data.
- In RL throughput on one RTX 4090, ETac supports 4,096 parallel environments and reaches 869 total FPS at that scale. The paper states this is 11× higher FPS and 128× more parallel environments than FEM on the same GPU.
- Reported total FPS for ETac is 669, 956, 878, 869 at 64, 256, 1024, 4096 environments. Taxim reaches 508, 620, 650 at 64, 256, 1024 and runs out of memory at 4096; TacSL reaches 698, 975, 918, 886.
- In blind grasping over four object types, the full-hand tactile setup reaches 84.45% ± 13.09 average success, compared with 72.90% ± 21.06 for fingertip sensors and 62.97% ± 37.82 for a non-tactile baseline using object pose. The paper highlights this as a 21.48 percentage point gain over the baseline.