Self-Models, Body Schemas, and Affordances › Limitations and Impact

Self-models are especially amenable to validation because the agent can choose some calibration actions and compare predictions with dedicated sensors. That advantage is bounded by safety, coverage, and sensor quality. Each layer still has characteristic failure modes.

The boundary choice is invisible when it is right and expensive when it is wrong. Place too little inside the self, for example by excluding a held tool, and the environment model must repeatedly infer action-coupled contact behavior. Place too much inside, such as the entire scene relative to the gripper, and the "self-model" stops transferring across tasks. In learned systems the boundary is implicit in which latents receive direct action inputs. A model that leaks policy information into environment latents may simulate its training distribution well and fail under interventions. Testing on shifted or off-policy action sequences is therefore a useful stress test for boundary choices. The symptom is not specific to boundary misallocation, however: poor coverage, optimization error, and other misspecification can cause the same failure, so follow-up interventions are needed to identify the cause.

Body schemas learned from data inherit the coverage of the agent's self-exploration. A schema trained near a nominal posture can be confidently wrong at extreme configurations. Calibration drift is the chronic version: gears wear, payloads change, temperatures shift, and a schema that was accurate at deployment can silently stop being so. The resilient-robotics result works because disagreement between prediction and sensing triggers a new identification cycle; long-lived embodied agents need an equivalent monitoring policy, and knowing when to distrust the self-model is as important as the model itself. Finite-sample affordance maps have a different evidential asymmetry. An observed success is a positive witness for that state-skill pair, whereas an unobserved success is not proof of impossibility. Negative conclusions require assumptions about exploration coverage and policy competence; dense sampling helps but does not supply those guarantees by itself. A planner that treats every empty cell as unreachable will forgo options, while one that ignores certified constraints will waste effort on hopeless actions. The corridor experiment gives a toy version of the same caution: its posterior reached certainty because the behavioral model was exactly right, while the same update under a wrong model could produce confident error.

Other-agent models carry the deepest limitations. Level-1 goal inference, like our corridor example, assumes the behavioral model is right; a real person sometimes moves away from their goal to avoid something, and the Bayesian update then confidently attributes the wrong goal. Nested reasoning compounds model error: to run level-2 inference, the agent must have a model of the other agent's model of itself, and errors multiply through the recursion. There are symmetric social failure modes: under-modeling people as physics produces safe-looking plans that are unusable around humans, while over-attributing intention (a learned model that sees goals in sliding doors and wind-blown bags) wastes computation and can produce bizarre avoidant behavior. Finally, theory-of-mind competence is hard to evaluate, because both the latent mental states and the ground truth for "understanding" are constructs; we will return to this in Part XI: Evaluation and Understanding. The evaluation problem is worth a closing note here, because it interacts with the four-way quality split from the opening: a model can pass behavioral theory-of-mind benchmarks, predictive fidelity on human trajectories, while its internal latent states bear no interpretable relationship to goals or beliefs, raising representation-quality questions the benchmarks never touch. Deciding how much that matters depends, once again, on whether the downstream use is prediction or explanation, and the field has not settled the answer.

The practical payoffs of these ideas are concrete. Model-based robot controllers rely on kinematic or dynamic models of the controlled system; these overlap with this chapter's broad body-schema concept, whether they are analytic, learned, or distributed across components. Learned and monitored schemas can turn some forms of damage into a re-estimation problem. Affordance models can make manipulation more tractable by pruning implausible actions and proposing useful contacts or skills, alongside geometric planning, optimization, feedback control, and learned policies. Models of other agents' trajectories, goals, or beliefs can improve crowd navigation and cooperation when interaction makes simpler reactive methods inadequate.