Robot Learning Glossary
Key concepts in imitation learning, reinforcement learning, and embodied AI — with links to datasets, models, and our data services.
Core Learning Concepts
Foundational concepts every robot learning team should align on.
CollectionTransfer & Deployment Concepts
Terms for bridging simulation and real-world execution.
CollectionLanguage-Conditioned Intelligence
Glossary track for VLA/VLM and instruction-following robotics.
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Terms & Definitions
Imitation Learning
Learning from demonstrations. Behavior cloning, DAgger, inverse RL. How robots learn from human teleoperation data.
Read more → CoreReinforcement Learning
Learning from trial and error. Reward signals, policy optimization. RL for robotics and sim-to-real.
Read more → ModelsVLA & VLM
Vision-Language-Action and Vision-Language Models. How VLMs enable language-conditioned robot control.
Read more → TransferSim-to-Real Transfer
Training in simulation, deploying in the real world. Domain randomization, reality gap, real-world data.
Read more → DataTeleoperation
Human-in-the-loop control for data collection. Bimanual, mobile, haptic. ALOHA, Mobile ALOHA.
Read more → CorePolicy Learning
Mapping observations to actions. ACT, Diffusion Policy, behavior cloning. Visuomotor policies.
Read more →Suggested Reading Paths
Shared Vocabulary
Build team alignment on key technical definitions.
Concept-to-Action Links
Each concept connects to practical implementation resources.
Faster Onboarding
New team members can quickly map terms to actual workflows.
Decision Clarity
Reduce ambiguity in model, data, and evaluation discussions.