Self-Models, Body Schemas, and Affordances › Key Parameters

The key parameters for the self-model implementation are:

• hidden_layer_sizes: Widths of the multilayer perceptron's hidden layers. Two layers of 64 units give enough capacity for the smooth two-dimensional kinematic map without overfitting the 2,000 babbling samples. The sizing logic is worth internalizing: capacity should scale with the complexity of the map being learned and the amount of excitation data available, and both are modest here, so a modest network is correct.
• max_iter: Maximum optimization iterations for the schema fit. A large budget lets the optimizer converge on this small dataset. On larger problems, more optimization time may improve the schema but delays its use, so the budget should follow the deployment constraint.
• random_state: Seed for the network's weight initialization, which keeps the learned schema reproducible across runs. Reproducibility matters pedagogically here because we quote specific error figures; in a deployed system the equivalent concern is variance across training runs, since a self-model that varies run to run is a self-model the agent cannot fully trust.
• n_train: Number of random motor-babbling configurations used to fit the schema. Coverage of the joint space, not the count alone, determines where the schema can be trusted. A larger sample drawn only from one region of joint space would yield a locally excellent, globally unreliable schema, which is the excitation lesson from System Identification restated in self-model terms.
• eps: Step size for the central finite differences that extract the Jacobian from the learned model. Larger steps increase truncation error, while very small steps amplify floating-point and evaluation noise. The useful range depends on local smoothness, arithmetic precision, and any stochasticity in model evaluation. In this example, model approximation error dominates across a broad epsilon range, so the chosen value should be read as an empirical error-budget choice rather than a universal optimum.
• grid_res: Workspace cell size for the reachability raster. Finer grids resolve the annulus boundary more sharply but require denser sampling for coverage. This parameter couples directly to the affordance map's reliability: at fixed sample count, refining the grid converts boundary cells from "probably reachable" to "unresolved," which is honest but less useful, and the choice is ultimately a decision-theoretic one about how much boundary detail the planner needs.