Probe-Diagnose-Adapt: An Evidence-Driven Embodied Research Loop for Robot Co-Design

Robot co-design is costly because evaluating a morphology typically requires training a controller for it. Each trial, however, reveals more than a task return: it shows how the body moves and interacts with its surroundings. How can these observations guide the next design change? We introduce Probe-Diagnose-Adapt (PDA), an LLM-guided framework that organizes robot co-design as an embodied research loop. PDA trains a fresh controller for each candidate under the same training budget and summarizes its behavior through module-level probes fixed after calibration. Drawing on this evidence and past trials, the LLM identifies a capability to investigate, proposes a feasible body edit, and predicts a measurable change before testing. Each experiment assesses task performance and the predicted change separately: performance guides morphology selection, while the prediction check informs subsequent reasoning. The resulting experimental memory preserves exact edits and both outcomes, allowing successful, rejected, and inconclusive trials to inform later proposals. Under matched per-body training budgets, PDA achieves the highest mean search performance at 53 morphologies on all six Evolution Gym tasks. On five tasks, these returns are competitive with the endpoints of longer baseline searches. Controlled ablations show complementary gains from behavioral evidence, module-level localization, and experimental memory. On two articulated-robot tasks in UNIMAL, the same loop improves on initial designs, supporting applicability beyond voxel-based morphologies. By linking edits to prospective predictions and measured outcomes, PDA takes a step toward embodied auto research through budgeted experimentation.