RevoSim: Scalable Multimodal Tactile Simulation for Dexterous Manipulation

Ke Zhang; Lixin Xu; Ziyi Wang; Xiyue Dong; Jie Tan; Lianghu Shao; Xianpeng Wang; Chuanyu Li

cs.RONov 6, 2026

RevoSim: Scalable Multimodal Tactile Simulation for Dexterous Manipulation

Authors: Ke Zhang, Lixin Xu, Ziyi Wang, Xiyue Dong, Jie Tan, Lianghu Shao, Xianpeng Wang, Chuanyu Li,

Organizations: HKUST(GZ) · BrainCo · Georgia Tech

Abstract

We present RevoSim, a scalable and high-fidelity multimodal tactile simulator for the BrainCo Revo3 hand that jointly models 285 piezoresistive taxels and five visuotactile fingertips. RevoSim efficiently generates synchronized pressure, depth, RGB, and marker-displacement observations; on a single RTX 5090, its depth-conditioned marker-displacement configuration supports up to 4,096 parallel environments. We train in-hand manipulation and grasping policies entirely in simulation, establishing a foundation for future sim-to-real transfer. Project page: https://braincotech.github.io/RevoSim-web/.

Figures & tables

Panel of Figure 1 — Fig. 1 : RevoSim observations and tactile-learning tasks.

Panel of Figure 2 — Fig. 2 : GPU-memory scalability of RevoSim.

Panel of Figure 3 — Fig. 3 : Qualitative visuo-tactile comparison of simulated appearance and marker motion with a real sensor response.

Panel of Figure 4 — Fig. 4 : Simulated and physical contact setups for piezoresistive sensing.

Panel of Figure 5 — Fig. 5 : Piezoresistive readouts: simulated whole-hand taxels and the physical index-pad response.

Tactile Simulation

See all 9 papers

Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation
Jul 6, 2026Zhe Zhao, Zhibin Li, Yilin Ou +1Robotic Grasping Dexterous Manipulation
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, China · University College London, United Kingdom
PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
Mar 4, 2026Rosy Chen, Mustafa Mukadam, Michael Kaess +4Dexterous Manipulation Sim-to-Real Transfer
Carnegie Mellon University · University of Washington · FAIR at Meta +1
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
Jun 21, 2026Trinity Chung, Kashu Yamazaki, Dhruv Patel +4Dexterous Manipulation Robotics Simulation
1Carnegie Mellon University · 2Genesis AI

Jul 6, 2026J/K move · Enter open · S save

Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation

Zhe Zhao, Zhibin Li, Yilin Ou, Mengshi Qi

State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, China · University College London, United Kingdom

Robotic Grasping Dexterous Manipulation In-Hand Manipulation Sim-to-Real Transfer Robotic RL Force-Controlled Robotic Manipulation

Open paper AskPDF arXiv

Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware due to contact-rich physics and imperfect actuation. We present a sim-to-real reinforcement learning method that leverages dense tactile feedback combined with joint torque sensing to explicitly regulate physical interactions. To enable effective sim-to-real transfer, we introduce (i) a computationally fast tactile simulation that computes distances between dense virtual tactile units and the object via parallel forward kinematics, providing high-rate, high-resolution touch signals needed by RL; (ii) a current-to-torque calibration that eliminates the need for torque sensors on dexterous hands by mapping motor current to joint torque; and (iii) actuator dynamics modeling with randomization to account for non-ideal torque-speed effects and bridge the actuation gaps. Using an asymmetric actor-critic PPO pipeline, we train policies entirely in simulation and deploy them directly to a five-finger hand. The resulting policies demonstrate two essential human-hand skills: (1) command-based controllable grasp force tracking and (2) reorientation of objects in the hand, both of which are robustly executed without fine-tuning on the robot. By combining tactile and torque in the observation space with scalable sensing and actuation modeling, our system provides a practical solution to achieve reliable dexterous manipulation. To our knowledge, this is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.

Abstract

Tactile Simulation

Explore similar work

Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation

PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation

Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks