A Reactive Planning Framework for Dexterous Robotic Manipulation

This thesis investigates a reactive motion planning and controller framework that enables robots to manipulate objects dexterously. We develop a robotic platform that can quickly and reliably replan actions based on sensed information. Robotic manipulation is subject to noise due to uncertainty in frictional contact information, and reactivity is key for robustness. The planning framework has been designed with generality in mind and naturally extends to a variety of robotic tasks, manipulators and sensors. This design is validated experimentally on an ABB IRB 14000 dual-arm industrial collaborative robot. In this research, we are interested in dexterous robot manipulation, where the key technology is to move an object from an initial location to a desired configuration. The robot makes use of a high resolution tactile sensor to monitor the progress of the task and drive the reactive behavior of the robot to counter mistakes or unaccounted environment conditions. The motion planning framework is integrated with a task planner that dictates the high-level manipulation behavior of the robot, as well as a low-level controller, that adapts robot motions based on measured tactile signaOutgoin

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

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas