How to Use DexBench for Humanoid Robot Dexterity Testing

Problem: Inconsistent Dexterity Measurements Across Humanoid Robots

Robotics teams worldwide struggle with a lack of common metrics for evaluating how well a humanoid robot can manipulate objects, balance, and perform fine‑motor tasks. Without a shared benchmark, comparing research results, hardware upgrades, or software improvements becomes a guessing game. This makes it hard to prove progress to investors, to decide which control algorithm wins, or to certify a robot for a specific application.

Prerequisites: What You Need Before Running DexBench

DexBench is a joint effort by RLWRLD and Nvidia, announced on June 9, 2026, to bring a single reference point for dexterity testing. To take advantage of the benchmark you should have the following in place:

• Humanoid robot platform with articulated arms, hands, and a torso that can be commanded via a standard API (e.g., ROS, custom SDK).
• Nvidia GPU that meets the minimum driver version recommended by Nvidia for the latest CUDA toolkit. The benchmark is designed to run on Nvidia hardware, so a compatible GPU is essential.
• Linux development environment (Ubuntu 22.04 or later) with Python 3.10+ installed. Most of the tooling around DexBench is scripted in Python.
• Access to the DexBench repository – RLWRLD will provide a download link or GitHub repo after you register your organization.
• Basic calibration data for the robot’s joint encoders, force‑torque sensors, and vision system. Accurate sensor readings are required for the benchmark’s precision tests.

If any of these items are missing, the benchmark will not produce reliable scores.

Steps: Running DexBench from Start to Finish

Below is a practical workflow that assumes you have satisfied the prerequisites.

1. Register and Download the Benchmark Suite

Visit the RLWRLD portal (the link is provided in the announcement) and fill out the short registration form. After approval you will receive a secure URL to clone the DexBench repository. Use git clone to pull the code onto your workstation.

2. Install Required Dependencies

Inside the cloned directory you will find a requirements.txt file. Run the following command to install Python packages and the Nvidia‑specific libraries:

pip install -r requirements.txt

The file includes torch, torchvision, and nvidia‑cublas-cu12 among others. Verify the installation by executing python -c "import torch; print(torch.cuda.is_available())". The output should be True.

3. Prepare Your Robot’s Software Interface

DexBench expects a thin wrapper that translates benchmark commands into robot motions. Create a Python module that implements the following functions:

• move_joint(joint_name, position, speed)
• grasp_object(object_id, force)
• read_sensor(sensor_name)

These functions should call your robot’s native SDK. If you already use ROS, you can expose the calls as ROS services and let the wrapper invoke rospy.wait_for_service.

4. Calibrate Sensors and Verify Kinematics

Run the calibration script included in tools/calibrate.py. The script will move each joint to its home position, record encoder offsets, and test force‑torque sensor baselines. Store the resulting calibration file (calib.yaml) in the config/ folder.

5. Select Benchmark Scenarios

DexBench ships with a set of pre‑defined tasks that stress different aspects of dexterity:

• Object Transfer – pick up a small cube, rotate it 90°, and place it on a target platform.
• Tool Use – grasp a screwdriver, insert it into a slot, and twist.
• Dynamic Balancing – lift a weighted tray while maintaining torso stability.

You can run the full suite or pick individual scenarios by editing benchmarks/config.yaml.

6. Execute the Benchmark

From the repository root, launch the benchmark runner:

python run_benchmark.py --config benchmarks/config.yaml

The script will sequentially load each scenario, command the robot via your wrapper, and log timestamps, joint trajectories, and sensor readings. All data is saved as JSON files in the results/ directory.

7. Analyze the Output

DexBench includes an analysis module that computes a composite dexterity score. Run:

python analyze_results.py --input results/ --output report.html

The generated HTML report shows:

• Task completion time.
• Positional error (mm) at each waypoint.
• Force compliance during grasp.
• Energy consumption measured via GPU power draw (Nvidia’s nvidia‑smi integration).

Compare your score against the baseline numbers published by RLWRLD. Those baselines represent a reference humanoid platform running the same tasks on an Nvidia Blackwell Ultra GPU.

8. Iterate and Document

Use the report to identify weak spots – for example, high positional error during tool insertion. Adjust your control algorithm, re‑run the calibration, and repeat the benchmark. Keep a changelog that links each iteration to a specific version of your software and the resulting score.

Pro Tips: Getting the Most Out of DexBench

• Leverage Nvidia profiling tools. While the benchmark already records GPU power, running nsight systems in parallel can reveal kernel bottlenecks in your perception pipeline.
• Automate regression testing. Wrap the run_benchmark.py call in a CI pipeline (GitHub Actions, GitLab CI) so every commit produces a new dexterity report.
• Use synthetic objects. If your lab lacks physical items, 3D‑print low‑cost replicas that match the dimensions listed in the benchmark’s object catalog.
• Share results with the community. RLWRLD encourages participants to upload anonymized scores to their leaderboard, fostering transparent comparison across labs.
• Monitor temperature. High‑performance Nvidia GPUs can throttle under prolonged load. Keep the cooling system clean and consider a brief cooldown between scenarios.

By following this workflow you turn DexBench from a headline announcement into a repeatable part of your development cycle, giving you a clear, comparable measure of how your humanoid robot handles real‑world dexterity challenges.