Imitation Learning
Learning from demonstrations — robots that replicate human behavior from teleoperation data.
What Is Imitation Learning?
Imitation learning (IL) is a paradigm where a robot learns to perform tasks by observing and replicating expert demonstrations. Instead of learning from reward signals (as in reinforcement learning), the robot learns from state-action pairs collected during human teleoperation or kinesthetic teaching.
Key Approaches
- Behavior Cloning (BC) — Supervised learning from (observation, action) pairs. Simple but prone to distribution shift.
- DAgger — Iterative data collection: run policy, get expert corrections, retrain. Reduces distribution shift.
- Inverse Reinforcement Learning (IRL) — Infer reward function from demonstrations, then optimize policy.
Related Resources
- Open-Source Datasets — DROID, BridgeData, ALOHA, Open X-Embodiment
- Policy Models — ACT, Diffusion Policy, OpenVLA, Octo
- Data Services — We collect learning-ready demonstrations for your tasks