Rock-and-walk manipulation : object transport by passive rolling dynamics and periodic active control

Abstract

When moving objects that are too bulky or heavy to be grasped or lifted, robotic manipulation can benefit from the object’s interaction with a support surface and its natural dynamics under gravity. This thesis presents the method of robotic rock-and-walk manipulation for dynamic, non-prehensile and underactuated object transport. The object, which is in contact with the support surface, is basically manipulated to rock from side to side about the contact point periodically by the robot system. In the meantime, the passive dynamics due to gravity enables the object to roll along a zigzag path that leads to a forward walk. This work is motivated by an interesting question in archaeology, how the giant statues of Easter Island (known as “moai”) were transported several hundred years ago, and a recent demonstration performed by archaeologists that it is possible to walk the statue by repetitive rocking. In this thesis, we first study the passive, dynamic behavior of a representative object model on a support surface and discuss its availability in relation to the geometric and mass properties of the object. Next, we separately develop model-based and learning-based control methods to realize dynamic rolling gait for object transport. In the model-based approach, we devise a feedback control strategy for steady object transport through effective regulation of its energy and posture by rock-and-walk manipulation. In the learning-based approach, we acquire a similar manipulation capability through reinforcement learning in a dynamic simulation environment that features the object and the support surface. An extensive set of experiments demonstrate the viability and practicality of our approaches in diverse settings in which the robots interact with the object in a non-prehensile manner. These include caging-based single-robot manipulation and cable-driven dual-robot manipulation using manipulator arms and aerial robots.

Date of Award	2021
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Jungwon SEO (Supervisor)