Real-World Data for ADROIT
ADROIT provides standardized evaluation for robot learning. Real-world data validates whether simulation performance transfers to physical hardware.
ADROIT at a Glance
Benchmark Profile
ADROIT (Autonomous Dexterous RObot In-hand manipulation Tasks) is a benchmark for dexterous manipulation using the Shadow Hand — a 24-DOF anthropomorphic robot hand. Developed at the University of Washington, it provides standardized tasks testing in-hand manipulation: pen spinning, valve turning, door opening, and object relocation.
The Sim-to-Real Gap
MuJoCo simulation simplifies the Shadow Hand's tendon-driven actuation with ideal joint models. Real tendon coupling, cable friction, and actuator backlash significantly change hand dynamics. Contact physics between fingertips and objects misses real tactile feedback and finger deformation.
Real-World Data Needed
Real Shadow Hand or similar dexterous hand manipulation recordings with full joint state and contact force data. Tactile feedback during in-hand manipulation. Diverse object sets for manipulation generalization.
Complementary Claru Datasets
Force-Torque Manipulation Dataset
Contact force data during manipulation provides the tactile signal that dexterous hand policies need.
Custom Dexterous Hand Collection
Recordings on physical dexterous hands capture real actuator dynamics, tendon coupling, and contact physics.
Bridging the Gap: Technical Analysis
ADROIT represents the gold standard for in-hand dexterous manipulation benchmarks. The Shadow Hand's 24 degrees of freedom provide human-level dexterity in simulation, but the gap between simulated and real Shadow Hand performance remains one of the widest in robotics.
The tendon-driven actuation of the real Shadow Hand creates dynamics that MuJoCo's ideal joint models cannot capture. Real tendons have coupling (moving one finger affects others through shared routing), friction (varying with speed and temperature), and compliance (stretching under load). These actuator dynamics mean that a torque command produces different motions on the real hand than in simulation.
The contact physics gap is equally severe for in-hand manipulation. Pen spinning requires controlled slip between fingertips and pen surface — a phenomenon that depends on fingertip material compliance, surface texture, and applied normal force in ways that contact simulation approximates poorly. Real manipulation data with force feedback at fingertips provides the ground truth for calibrating contact models.
ADROIT's influence extends beyond its specific tasks. Many reinforcement learning methods are first validated on ADROIT tasks before being applied to real dexterous hardware. Real-world comparison data ensures these methods transfer rather than simply exploiting simulation artifacts.
Frequently Asked Questions
ADROIT (Autonomous Dexterous RObot In-hand manipulation Tasks) is a benchmark for dexterous manipulation using the Shadow Hand — a 24-DOF anthropomorphic robot hand. Developed at the University of Washington, it provides standardized tasks testing in-hand manipulation: pen spinning, valve turning, door opening, and object relocation.
Real Shadow Hand or similar dexterous hand manipulation recordings with full joint state and contact force data. Tactile feedback during in-hand manipulation. Diverse object sets for manipulation generalization.
MuJoCo simulation simplifies the Shadow Hand's tendon-driven actuation with ideal joint models. Real tendon coupling, cable friction, and actuator backlash significantly change hand dynamics. Contact physics between fingertips and objects misses real tactile feedback and finger deformation.
Yes. Claru coordinates data collection on specific robot platforms and in specific environments to enable direct comparison between simulated and real performance for ADROIT tasks.
Get Real-World Data for ADROIT
Discuss purpose-collected data to validate and improve your ADROIT-trained policies on physical hardware.