Caltech

Enhancing Planning and Decision Making for Robotic Autonomy - John Lathrop

John Lathrop’s Everheart lecture tackled the pressing challenge of planning and decision‑making for robotic autonomy when data are scarce. He illustrated the problem with three real‑world projects—a custom tilt‑jet VTOL aircraft, an autonomous racing car, and a simulated spacecraft‑capture mission—each of which lacks the large datasets that power most machine‑learning pipelines. The talk contrasted two dominant paradigms. Model‑based planning relies on explicit dynamics and optimization but quickly becomes intractable in high‑dimensional, disturbance‑rich environments. Deep‑learning approaches, by contrast, deliver millisecond‑scale inference after offline training, yet they falter when confronted with out‑of‑distribution conditions such as unexpected wind, rain, or novel terrain. Lathrop highlighted a hybrid solution: a spectral‑expansion decision‑tree algorithm that embeds learned priors into the planning process. In a live demo, a quadrotor navigated four targets amid complex flow fields and obstacles, solving the problem in real time on a laptop without prior training data. This example underscored how compact representations can bridge the gap between model‑based rigor and deep‑learning speed. The broader implication is clear: future autonomous systems will need to fuse model‑based control with learned representations to remain robust under data scarcity. By advancing hybrid methods, researchers can expand out‑of‑distribution performance, accelerate real‑time planning, and unlock safe deployment of novel robots in unpredictable environments.