DOT-Sim uses MPM physics plus learned residual optics to simulate deformable tactile sensors, supporting zero-shot sim-to-real transfer for classification and control tasks.
Difftactile: A physics-based differentiable tactile simulator for contact-rich robotic manipulation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.
ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
EUPHORIA is a hybrid framework using meta-learning via graph hypernetworks, physics-biased attention in graph transformers, and residual stability correction for few-shot adaptable robotic assembly planning.
citing papers explorer
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DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration
DOT-Sim uses MPM physics plus learned residual optics to simulate deformable tactile sensors, supporting zero-shot sim-to-real transfer for classification and control tasks.
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Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation
Wiggle and Go! uses system identification from rope motion observations to predict parameters that enable zero-shot goal-conditioned dynamic manipulation, achieving 3.55 cm accuracy on 3D target striking versus 15.34 cm without parameter information.
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ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation
ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
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EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
EUPHORIA is a hybrid framework using meta-learning via graph hypernetworks, physics-biased attention in graph transformers, and residual stability correction for few-shot adaptable robotic assembly planning.