This study presents the technique of robotic high-speed scooping: rapidly picking an object lying on a support surface by making contact with the object's open top face and the bottom face that is hidden in contact with the support surface. Essential to high-speed scooping is thus to make suitable dynamic, impactful interaction happen among the robot, object, and environment under errors and uncertainties. We propose a solution to this challenge based on stiffness control, an approach for indirect force control using the robot that is arranged to behave like a desired mechanical system. An implementation of the solution is then presented using a custom-built two-fingered direct-drive gripper. Our experiments verify that high-speed scooping operation is achievable, with the duration of dynamic interaction less than 0.3 s, and effective to various scooping situations featuring objects durable and fragile.

When moving objects that are too bulky or heavy to be grasped or lifted, robotic manipulation can benefit from the object's interaction with the support surface and its natural dynamics under gravity. In this work, we show that such dynamic, underactuated manipulation capability can be acquired through reinforcement learning and deployed on real robot systems. First, we present a framework to learn a control policy for object transport in a dynamic simulation environment, featuring the object and the support surface. We then demonstrate successful object locomotion with the learned policy through a set of simulated and real-world experiments, performed with a robot arm and an aerial robot interacting with the object in a non-prehensile manner. While the object, which is in contact with the support surface, oscillates sideways passively under gravity, the robot uses the learned policy to move the object forward with a steady gait by regulating the mechanical energy and the posture of the object. Our experiment results show that the learned policy can transport the object through unmodeled effects of terrain and perturbation.

This study presents the method of roboticrock-and-walkmanipulation for dynamic, nonprehensile object locomotion. The object, which is in contact with an environmental 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. Rock-and-walk is a special-purpose method that enables the transport of a certain class of objects, which are too large, heavy to apply other primary methods such as grasping- or pushing-based operations. Our 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 periodic rocking. We present a detailed study of the dynamics, kinematics, and mechanics of the object-robot-environment system, and devise a feedback control strategy for rock-and-walk gaiting through the effective regulation of the object’s energy and posture. An extensive set of experiments demonstrate the viability and practicality of our approach in diverse settings: Caging-based single-robot manipulation and cable-driven dual-robot manipulation using manipulator arms and aerial robots.

With the fast growth of the knowledge bases built over the Internet, storing and querying millions or billions of RDF triples in a knowledge base have attracted increasing research interests. Although the latest RDF storage systems achieve good querying performance, few of them pay much attention to the characteristic of dynamic growth of the knowledge base. In this paper, to consider the efficiency of both querying and incremental update in RDF data, we propose a hAsh-based tWo-tiEr rdf sTOrage system (abbr. to AWETO) with new index architecture and query execution engine. The performance of our system is systematically measured over two large-scale datesets. Compared with the other three state-of-the-art RDF storage systems, our system achieves the best incremental update efficiency, meanwhile, the query efficiency is competitive.