Robot Learning
Observation Space
An observation space is the formal specification of what a policy perceives at each timestep: the set of sensor modalities, their shapes, ranges, and encodings, such as RGB images from specified cameras, joint positions, gripper state, and force-torque readings. It defines the input interface of a learned policy, mirroring the action space on the output side.
Why it matters for physical AI
Policy transfer between simulation, datasets, and hardware requires exactly matching observation spaces, and modality choices, such as adding wrist cameras or tactile input, strongly shape learnability.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.