# System Identification

When a camera moves or a controller is replaced, the same command moves the robot in a different direction or by a different magnitude, while a frozen world model still plans under its training-time action convention, so every plan is expressed in the wrong action frame. Library of Hands (LoH) trains nothing and leaves the world model frozen: it uses the frozen predictor to infer the applied action from two successive camera images, estimates the map from commands to applied actions, and stores each identified map in a library for reuse. In closed-loop tests where hands change and come back, LoH raises a frozen planner's success from 28.6% to 62.3%, and loses only 0.6 percentage points when nothing changes.

When a camera moves or a controller is replaced, the same command moves the robot in a different direction or by a different magnitude, while a frozen world model still plans under its training-time action convention, so every plan is expressed in the wrong action frame. Library of Hands (LoH) trains nothing and leaves the world model frozen: it uses the frozen predictor to infer the applied action from two successive camera images, estimates the map from commands to applied actions, and stores each identified map in a library for reuse. In closed-loop tests where hands change and come back, LoH raises a frozen planner's success from 28.6% to 62.3%, and loses only 0.6 percentage points when nothing changes.

world2vec learns, from observations and actions alone and without reward information, an encoder that maps physical state into latent coordinates plus latent dynamics describing how that state evolves. It requires the encoded future to match what rolling out the latent dynamics predicts, a condition computable from observations and actions alone. On Pendulum, Reacher and Cartpole it performs pixel-based continuous control with at least an order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM, and identifies systems across a benchmark of 71 attractors.

world2vec learns, from observations and actions alone and without reward information, an encoder that maps physical state into latent coordinates plus latent dynamics describing how that state evolves. It requires the encoded future to match what rolling out the latent dynamics predicts, a condition computable from observations and actions alone. On Pendulum, Reacher and Cartpole it performs pixel-based continuous control with at least an order of magnitude fewer parameters than DreamerV3, TD-MPC2 and LeWM, and identifies systems across a benchmark of 71 attractors.