Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

Nan Wang; Mohit Yadav; Jonathan Wulff; Aidan Rosenbaum; Kezhou Chen; Yuvan Sharma; Xu Dong; Yiwei Tao

cs.ROAug 28, 2026

Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

Authors: Nan Wang, Mohit Yadav, Jonathan Wulff, Aidan Rosenbaum, Kezhou Chen, Yuvan Sharma, Xu Dong, Yiwei Tao

Organizations: Chestnut Robotics

Abstract

Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effects produce that saving. Routing force through a cable removes the requirement that a motor fit inside the joint it drives, so smaller and cheaper motors suffice, and one motor can drive several joints through a single cable, so fewer motors are needed. They are also harder to learn on than a direct-drive hand. The underactuated transmission that produces the saving is itself difficult to represent in a simulator, and the joints one cable drives are not independently commandable. We present Aero Hand Open, a tendon-driven anthropomorphic hand that is released simulation-ready. Three things ship with it. A simulation model reproduces the cable transmission itself. An identified actuation map connects that model to the motor commands in both directions, including the three-way coupling of the thumb. A reinforcement learning package trains policies for the hand. Together they let a policy be trained entirely in simulation and run on the hand with no fine-tuning and no state estimation. We release the mechanical design, the simulation model, the identified mapping, the training environment and the deployment stack.

Figures & tables

Hand	Drive	Fingers	DoFs	Mass [g]	Price [$]
InMoov	Tendon	5	5	750	$\sim 200$
Allegro V5 Plus	Servo	4	16	1,024	17,000
ISyHand	Servo	4	18	620	$\sim 1,300$
ILDA Hand	Linkage	5	15	1,100	NA
LEAP Hand	Servo	4	16	600	2,000
Shadow Hand	Tendon	5	20	4,300	$>100,000$

Table 1: Comparison of existing dexterous hands. DoFs denotes the number of actuated degrees of freedom, and NA marks a figure the manufacturer does not publish.

	Amplitude		$10$ – $90\%$ rise time
Channel	Excursion $\|\Delta\|$	Tracking RMS	Simulation	Hardware
Thumb CMC abduction (joint) [mrad]	$124.94$	$26.79$	$70$ ms	$80$ ms
Thumb CMC flexion cable [mm]	$5.42$	$0.11$	$40$ ms	$80$ ms
Thumb flexor cable [mm]	$6.44$	$0.63$	$60$ ms	$100$ ms
Index cable [mm]	$0.04$	$0.45$	$90$ ms	$120$ ms
Middle cable [mm]	$0.32$	$0.39$	$90$ ms	$120$ ms

Table 2: Sim-to-real validation of the transmission model. Excursion is the difference between the model and the hardware in the cable travel from the extended hand to a fist, measured independently on each system. Tracking RMS is the residual between the simulated sensor and the encoder-derived actuation value over a

134

s run in which one command stream drives both systems, evaluated on settled samples only. The rise times are medians over

72

–

177

matched steps per channel. Cable channels are reported in millimetres and the abduction joint in milliradians. Simulated rise times are reconstructed from the integrator step count, since the acquisition loop advances the simulator at about half real time; the

10

ms simulation timestep and the

20

ms hardware sampling period bound their resolution.

Parameter	Distribution
Fingertip friction (5 geoms)	$\mathcal{U}(0.5,\,1.0)$
Cube inertia	$\times\,\mathcal{U}(0.8,\,1.2)$
Cube centre-of-mass offset	$+\,\mathcal{U}(-5,\,5)$ mm
Joint zero offsets (16)	$+\,\mathcal{U}(-0.05,\,0.05)$ rad
Joint dry friction (16)	$\times\,\mathcal{U}(0.5,\,2.0)$
Joint armature (16)	$\times\,\mathcal{U}(1.0,\,1.05)$

Table 3: Domain randomisation actually applied during training.

Setting	Training	Deployment
Control rate	$20$ Hz	$20$ Hz
Cable action scale $s_{1:4}$	$0.020$ m	$0.026$ – $0.024$ m
Thumb scales $s_{5:7}$	$0.7$ , $0.003$ , $0.012$	identical
Nominal $\ell^{0}_{1:4}$	$0.090$ m	$0.083$ – $0.086$ m
Nominal $\ell^{0}_{5:7}$	$0.75$ , $0.035$ , $0.1$	identical
Observation noise	$\pm 5$ mm, $\pm 0.05$ rad	none

Table 4: The learned controller is transferred unchanged; these are the only differences between the training environment and the Robot Operating System (ROS 2) node that runs on the hand. The slightly larger cable scales and the slightly more closed nominal pose were retuned on the physical hand.

Explore similar work

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 +1Contact-Rich Dexterous Manipulation Sim-To-Real Reinforcement Learning
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, China · University College London, United Kingdom
MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation
Apr 19, 2026Zhuoheng Li, Qingquan Lin, Checheng Yu +5Robotic Hands Dexterous Manipulation
The University of Hong Kong · City University of Hong Kong · XiFengGu Ltd.
MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand
Jul 16, 2026Alvin Zhu, Mingzhang Zhu, Beom Jun Kim +3Bimanual Manipulation Teleoperation
Department of Computer Science, UCLA, Los Angeles, CA, USA. · Department of Electrical and Computer Engineering, UCLA, Los Angeles, CA, USA. · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA.

Jul 6, 2026, cs.ROJ/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

Contact-Rich Dexterous Manipulation Sim-To-Real Reinforcement Learning Dexterous Manipulation Bimanual Manipulation Tactile Torque+1

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

Explore similar work

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

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand