Glossary
What Is Robot Learning?
Last reviewed: September 2026
Short answer
Robot learning trains a policy — the function that maps what a robot senses to what it does next — from data, instead of hand-coding rules for every situation. The two dominant approaches are imitation learning, which copies demonstrated behavior, and reinforcement learning, which optimizes a reward signal. Either way, the policy is only as good as the data or reward it learned from.
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Why it matters
A hand-coded robot only works in the exact situation its rules were written for. Robot learning lets a policy generalize from examples instead, but that generalization has a ceiling set by the training data: its viewpoint, its coverage of edge cases and failures, and whether it matches the embodiment the policy will actually run on.
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How Firsthand handles it
Firsthand supplies the demonstration data robot-learning pipelines train on — first-person video, depth, 3D hand pose, and action segments, hardware-synchronized onto one clock — collected fast, on demand, and to a written spec, then delivered in RLDS, LeRobot format, or WebDataset.
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The main ways a robot policy is trained, and what each needs from a dataset
| Approach | What the policy learns from | What the dataset needs to provide |
|---|---|---|
| Imitation learning | Demonstrated trajectories, no reward function | Aligned observation-action pairs, at the fidelity the policy sees at inference |
| Behavioral cloning | Direct observation → action prediction at each step | Same as imitation learning; the simplest form of it |
| Vision-language-action (VLA) training | Vision, a language instruction, and the action that carried it out, on one timeline | Synchronized video, task instruction, and action stream in the same episode |
| World-model training | How the scene changes in response to an action, as a prediction target | Dense video plus depth and action/pose signals, causally linked on the same clock |
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Frequently asked questions
- What is robot learning?
- Robot learning is training a robot’s behavior from data rather than writing rules by hand. A policy is trained to map sensor input to action, either by copying demonstrations (imitation learning) or by optimizing a reward (reinforcement learning).
- What is the difference between robot learning and imitation learning?
- Robot learning is the broad field. Imitation learning is one method within it — training a policy to copy demonstrated behavior. See imitation learning and behavioral cloning for how that specific method works.
- What data does robot learning need?
- It depends on the method, but most approaches need aligned observation and action data at every timestep — egocentric or teleoperated video, depth, hand pose, or robot action logs, all on a shared clock so the observation at time t actually corresponds to the action at time t.
- Why does data quality matter so much for robot learning?
- A learned policy can only be as good as the data it trained on. Narrow viewpoints, missing failure cases, or an embodiment mismatch between the training data and the target robot all become gaps the policy inherits. See why failure cases matter in robot learning.
- Does robot learning always use human demonstrations?
- No. It can also learn from teleoperated robot data or simulation. See teleoperation vs egocentric data for how human-demonstration and teleoperation sources compare.