Scaling Data for Robot GPTs

Introduction

Robot GPTs require massive trajectories; scale via fleets/sims to years of data.

Key Concepts

• Data Hunger: 10k-100k hours for dexterity.
• Fleet Scaling: Parallel robots collecting diverse episodes.
• Sim-to-Real: Bridge gaps with domain randomization.

Implementation Steps

1. Fleet Setup:

   from robotics import Fleet
   fleet = Fleet(num_robots=100)
   data = fleet.collect_trajectories(tasks=['grasp', 'navigate'])
   

2. Sim Integration:

   import mujoco
   

Or Isaac Gym

sim_data = simulate(10000_episodes)
real_data = domain_randomize(sim_data)

3. **Video Aug**: Extract actions from human clips via pose estimation.
4. **Pipeline**: Dedup, balance, fine-tune model.

## Example\n\nScale navigation data: Deploy 200 sim agents + fleet of 50 physical units to collect 500k episodes; augment with 10k human videos; achieve 90% sim-to-real transfer in unseen environments.

## Evaluation
- Metrics: Success rate in unseen tasks.
- Trade-offs: Cost of fleets vs. sim quality.

## Conclusion

Hybrid data scaling enables generalist robots; consortia accelerate via shared pools.