TactileLab: せん断感覚を効率的にシミュレートする触覚シミュレーションによるSim-to-Real巧緻マニピュレーション
TactileLab: Sim-to-Real Dexterous Robot Manipulation with Efficient Shear-Sensitive Tactile Simulation
剛体シミュレーションで接触界面のせん断流れ場を効率的に生成する触覚シミュレーションフレームワークTactileLabを提案し、実触覚画像をシミュレーション触覚流れ場へ変換するreal-to-sim手法と組み合わせて実機での巧緻マニピュレーションを実現した。
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著者: Yijiong Lin, Wenhao He, Toby Jack, Max Yang, Haoran Li, Nathan F. Lepora
分類: cs.RO
原文アブストラクト
Robotic simulators that represent shear within tactile sensing are becoming increasingly important for contact-rich manipulation. However, simulating contact shear with high-fidelity deformable or soft-body simulation is computationally demanding, limiting scalability to large-scale reinforcement learning. Here we introduce \textit{TactileLab}, a scalable multimodal tactile simulation framework built on IsaacLab, with a particular focus on a new optical-flow-based tactile representation termed \textit{simulated tactile flow}. Instead of relying on soft-body physics to model shear deformation, TactileLab uses rigid-body simulation to efficiently estimate dense tangential motion at the contact interface, producing tactile flow fields that encode shear-relevant contact dynamics. This flow field complements geometric features from a contact depth field. We further introduce a novel policy architecture for jointly processing long-horizon proprioceptive signals and rich tactile observations. Across multiple contact-rich tasks, policies trained using simulated tactile flow learn efficiently at scale and exhibit emergent contact-aware behaviours. To facilitate sim-to-real policy deployment, we then propose a novel real-to-sim tactile translation method that maps real tactile image streams to simulated tactile flow and depth fields, together with a new tactile data collection method, and validate it across diverse tactile sensor designs. Using this pipeline, we demonstrate real-world deployment on tasks encompassing dual-arm and dexterous in-hand manipulation. Overall, TactileLab provides an end-to-end sim-to-real framework for scalable tactile policy learning, enabling diverse shear-sensitive manipulation across heterogeneous tactile robotic systems.
関連論文
- TactSpace: 触覚のシミュレーションから実世界への転移のための物理強化共有潜在空間の学習触覚/sim2real
- HydroShear: 触覚シミュレーションから実機への強化学習のためのハイドロエラスティックせん断シミュレーション触覚/sim2real
- 触覚シミュレータを実写類似・ゼロショット性能で拡張するSightGAN触覚/sim2real
- Tactile Gym 2.0:低コスト高解像度触覚センサを比較するためのSim-to-Real深層強化学習触覚/sim2real
- 深層テクスチャ生成ネットワークによる触覚Sim2Realギャップの削減触覚/sim2real
- 物理シミュレーションと学習潜在投影による触覚センシングのsim-to-real触覚/sim2real