Into:Swarm
The open benchmark for autonomous drone flight.
As of · Jun 4, 10:37 UTC
An open benchmark where AI learns to fly drones through 1,000 procedurally generated 3D worlds, using nothing but a depth camera and raw flight state. No maps, no shortcuts, just raw skill.
What is Swarm
Swarm is a Bittensor that turns autonomous drone navigation into an open competition. train neural networks to fly drones through simulated 3D environments, while score their performance based on how quickly and accurately they reach a landing platform.
The simple version: Think of it as a robotics benchmark, like how ImageNet standardized object detection, but for drone flight. You give the AI a depth camera image and a flight state, and it must figure out how to navigate to a goal, no prior knowledge of the environment allowed.
Centralized equivalent: There is no direct open equivalent. Proprietary drone flight algorithms are typically locked behind NDAs and corporate R and D departments, making fair comparison impossible.
How it works:
- Miners submit pre-trained reinforcement learning policies (any framework, Stable Baselines 3, custom, whatever works) that must navigate drones through unseen environments using only a 128x128 depth image and a state vector.
- Validators generate random MapTasks with start-to-goal pairs, evaluate miner policies headless in a PyBullet physics simulator, and score based on success (50% weight) and time efficiency (50% weight).
Why This Matters
Other research from the same neighborhood of the network.
