Agents' Last Exam
for Robotics
Benchmark autonomous agents on actual robotics problems in sandboxes, with authoritative metrics computed by independent evaluator containers.
Four commitments that make results meaningful
ALE Robotics is built so that a number on the leaderboard actually means what it says.
Open-set problem solving
Agents implement a paper's method or pursue open-ended optimization from a paper and a simulation environment — not a fixed multiple-choice answer key.
Evaluator-computed metrics
The authoritative domain metric is computed inside an independent evaluator container, never self-reported by the agent under test.
Hidden evaluation protocols
Official evaluation runs on hidden seeds and private references. Agents interact only through gym-over-gRPC.
Reproducible & auditable runs
Every run produces a signed, hash-chained bundle. Scores are recomputed server-side, and verification is honest about what was actually checked.
Organized by platform and research direction
Every task is classified along platform, research direction, simulator, evaluation mode, and headline metric.
Manipulation
5- Diffusion PolicyLearning
- Robomimic BC-RNNLearning
- ACTLearning
- DP3Learning
- EDMPPlanning
UAV
2- Drone HoverControl
- EGO-SwarmPlanning
Vehicle
2- BARN DWANavigation
- CrowdNav SARLNavigation
Quadruped
2- ANYmal FlatLocomotion
- Go1 JoystickLocomotion
Interactive & batch
Interactive tasks step a live environment over gym-over-gRPC, episode by episode. Batch tasks run a full offline evaluation job and return aggregate results.
How modes work →Normalized, anchored to papers
The evaluator computes the domain metric; the harness normalizes it against a paper anchor. Aggregate uses a configurable macro_mean. Scores can exceed 1.0 and are never forced into a percentage.
Scoring model →Honest by construction
Bundles are decrypted, schema-checked, hash-chain verified, and rescored on the server. Passing automatic validation is not official verification — the site is explicit about the difference.
Verification levels →Start here
Diffusion Policy
- Evaluator
- robosuite + robomimic
- Metric
- Success rate
- Paper target
- 0.92
Drone Hover
- Evaluator
- gym-pybullet-drones
- Metric
- Episode reward
- Paper target
- 474
BARN DWA
- Evaluator
- BARN / Gazebo
- Metric
- Success rate
- Paper target
- 0.88
ANYmal Flat
- Evaluator
- legged_gym / Isaac
- Metric
- Episode reward
- Paper target
- 19.55
Add a paper, a simulator, or an agent integration.
ALE Robotics grows through community contributions across three paths, each with clear quality and safety gates.

