Mechanics of Robotic Manipulation

By Matthew Thomas Mason. 2001

541 citations recorded by the source corpus.

"Manipulation" refers to a variety of physical changes made to the world around us. Mechanics of Robotic Manipulation addresses one form of robotic manipulation, moving objects, and the various processes involved---grasping, carrying, pushing, dropping, throwing, and so on. Unlike most books on the subject, it focuses on manipulation rather than manipulators. This attention to processes rather than devices allows a more fundamental approach, leading to results that apply to a broad range of devices, not just robotic arms. The book draws both on classical mechanics and on classical planning, which introduces the element of imperfect information. The book does not propose a specific solution to the problem of manipulation, but rather outlines a path of inquiry.

Subfields: Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a05a1ccc833579eeb8d1fabccffb2e61788ea0c2

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In Biomedical Engineering

In Control and Systems Engineering

Citations in the corpus

Planar Microassembly by Parallel Actuation of MEMS Microrobots (2008, methodology): “We use the term compliance to denote the change of pose of one rigid body in accommodation to forces exerted by a second rigid body and friction [3].”
Predictive Robot Programming: Theoretical and Experimental Analysis (2004, background): “Three common descriptions include rotation matrices, Euler angles, and unit quaternions (Mason 2001).”
Planning algorithms (2006, methodology): “It can be shown that all momentum expressions extend to sums over the particles [684].”
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, unknown): “…any competencies it requires can be considered as enabling specific ways to interact with and affect the physical world , which are further divided into mobility —moving in the environment—and manipulation —moving or rearranging (e.g., grasping, rotating) objects in the environment (17, 18, 19).”
More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing (2016, background): “The quasistatic assumption makes the problem more tractable, yielding simpler models, and is a reasonable assumption for the scales and speeds in much of robotic manipulation [22].”
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control (2015, background): “Vision-based robotic manipulation is the process by which robots use their manipulators (such as robotic arms) to rearrange environments [Mason, 2001], based on camera images.”
Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation (2008, background): “We distinguish between three categories of approaches to manipulation [2, 15, 17].”