Robot Learning

Action Space

An action space is the set of all commands a policy can issue to a robot, together with their parameterization. Common manipulation choices include joint positions, joint velocities, joint torques, and Cartesian end-effector deltas plus a gripper command; locomotion policies typically output joint position targets tracked by low-level PD controllers. The space may be continuous, discrete, or a hybrid, and its frame, dimensionality, and rate shape what behaviors are learnable.

Why it matters for physical AI

Action space choice often matters as much as architecture: end-effector deltas transfer more easily across embodiments, while joint-space actions permit finer whole-arm control, directly affecting cross-robot generalization of foundation policies.