This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.

ABSTRACT

Data-driven machine learning methods are making advances in many long-standing problems in robotics, including grasping, legged locomotion, perception, and more. There are, however, robotics applications for which data-driven methods are less effective, and sometime inappropriate. Data acquisition can be expensive, time consuming, or dangerous — to the surrounding workspace, humans in the workspace, or the robot itself. In such cases, generating data via simulation might seem a natural recourse, but simulation methods come with their own limitations, particularly when nondeterministic effects are significant, or when complex dynamics are at play, requiring heavy computation and exposing the so-called sim2real gap. Another alternative is to rely on a set of demonstrations, limiting the amount of required data by careful curation of the training examples; however, these methods fail when confronted with problems that were not represented in the training examples (so-called out-of-distribution problems), and this precludes the possibility of providing provable performance guarantees.

In this talk, I will describe recent work on robotics problems that do not readily admit data-driven solutions, including flapping flight by a bat-like robot, vision-based control of soft continuum robots, acrobatic maneuvering by quadruped robots, a cable-driven graffiti-painting robot, bipedal locomotion over granular media, and ensuring safe operation of mobile manipulators in HRI scenarios. I will describe some specific difficulties that confront data-driven methods for these problems, and describe how model-based approaches can provide workable solutions. Along the way, I will also discuss how judicious incorporation of data-driven machine learning tools can enhance performance of these methods.