What RoboMetrics Is And Isn't

RoboMetrics is a local metrics library for robotics evaluation. It is designed to sit after simulation, data collection, or policy rollout code and turn trajectory-like outputs into repeatable metric results.

Scope Boundary

Need RoboMetrics role Use another tool for
Compute ADE, FDE, safety, comfort, coverage, calibration, physics, and task metrics Yes Defining the physical world or sensor stack
Evaluate CSV, JSON, NumPy, or lightweight adapter exports Yes Storing large datasets or streaming bag files directly
Run a deterministic CI regression check Yes Running distributed benchmarks or public leaderboard governance
Compare two local policy result files Yes Hosting dashboards or experiment databases
Parse small ROS/ROS 2 JSON exports without ROS imports Yes Decoding arbitrary ROS middleware state or running ROS nodes
Simulate robot dynamics, contacts, or scenes No Isaac Sim, MuJoCo, Gazebo, Drake, PyBullet, or project simulators
Train policies or optimize controllers No PyTorch, JAX, RL libraries, MPC stacks, or imitation-learning frameworks
Manage dataset lineage and labeling No Dataset/versioning platforms and lab data infrastructure

Use RoboMetrics When

  • You already have policy predictions, rollout trajectories, reference paths, actor trajectories, or time-series arrays.
  • You need typed metric functions that are small enough to run in unit tests or CI.
  • You want strict JSON output for downstream CI consumers.
  • You want project-local thresholds without adopting a full benchmark platform.

Do Not Use It As

  • A simulator.
  • A robotics middleware.
  • A training framework.
  • A dataset host.
  • A public leaderboard service.
  • A substitute for domain-specific validation of sensor, contact, or actuation models.

The clean boundary is: generate or export rollouts elsewhere, then use RoboMetrics to measure them.