Dexterous Manipulation Graphs

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation

Toward Dynamic Manipulation of Flexible Objects by High-Speed Robot System: From Static to Dynamic

This chapter explains dynamic manipulation of flexible objects, where the target objects to be manipulated include rope, ribbon, cloth, pizza dough, and so on. Previously, flexible object manipulation has been performed in a static or quasi-static state. Therefore, the manipulation time becomes long, and the efficiency of the manipulation is not considered to be sufficient. In order to solve these problems, we propose a novel control strategy and motion planning for achieving flexible object manipulation at high speed. The proposed strategy simplifies the flexible object dynamics. Moreover, we implemented a high-speed vision system and high-speed image processing to improve the success rate by manipulating the robot trajectory. By using this strategy, motion planning, and high-speed visual feedback, we demonstrated several tasks, including dynamic manipulation and knotting of a rope, generating a ribbon shape, dynamic folding of cloth, rope insertion, and pizza dough rotation, and we show experimental results obtained by using the high-speed robot system

Modeling and Control of Multi-Arm and Multi-Leg Robots: Compensating for Object Dynamics during Grasping

Dehio N, Smith J, Wigand DL, et al. Modeling & Control of Multi-Arm and Multi-Leg Robots: Compensating for Object Dynamics during Grasping. In: IEEE/RSJ Int. Conf. on Robotics and Automation. 2018

Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin