Title: Learning Flexible and Reusable Locomotion Primitives for a Microrobot,
Abstract: The design of gaits for robot locomotion can be a daunting process which
requires significant expert knowledge and engineering. This process is even
more challenging for robots that do not have an accurate physical model, such
as compliant or micro-scale robots. Data-driven gait optimization provides an
automated alternative to analytical gait design. In this paper, we propose a
novel approach to efficiently learn a wide range of locomotion tasks with
walking robots. This approach formalizes locomotion as a contextual policy
search task to collect data, and subsequently uses that data to learn
multi-objective locomotion primitives that can be used for planning. As a
proof-of-concept we consider a simulated hexapod modeled after a recently
developed microrobot, and we thoroughly evaluate the performance of this
microrobot on different tasks and gaits. Our results validate the proposed
controller and learning scheme on single and multi-objective locomotion tasks.
Moreover, the experimental simulations show that without any prior knowledge
about the robot used (e.g., dynamics model), our approach is capable of
learning locomotion primitives within 250 trials and subsequently using them to
successfully navigate through a maze.