Mobility and Manipulation

Abstract

Mobile manipulation requires the integration of methodologies from all aspects of robotics. Instead of tackling each aspect in isolation, mobile manipulation research exploits their interdependence to solve challenging problems. As a result, novel views of long-standing problems emerge. In this chapter, we present these emerging views in the areas of grasping, control, motion generation, learning, and perception. All of these areas must address the shared challenges of high-dimensionality, uncertainty, and task variability. The section on grasping and manipulation describes a trend towards actively leveraging contact and physical and dynamic interactions between hand, object, and environment. Research in control addresses the challenges of appropriately coupling mobility and manipulation. The field of motion generation increasingly blurs the boundaries between control and planning, leading to task-consistent motion in high-dimensional configuration spaces, even in dynamic and partially unknown environments. A key challenge of learning for mobile manipulation consists of identifying the appropriate priors, and we survey recent learning approaches to perception, grasping, motion, and manipulation. Finally, a discussion of promising methods in perception shows how concepts and methods from navigation and active perception are applied.

2-D

two-dimensional

3-D

three-dimensional

CRF

conditional random field

DMP

dynamic movement primitive

DOF

degree of freedom

GP

Gaussian process

HMM

hidden Markov model

HOG

histogram of oriented features

JPL

Jet Propulsion Laboratory

KNN

k-nearest neighbor

NDT

normal distributions transform

OM

occupancy map

POMDP

partially observable Markov decision process

PRM

probabilistic roadmap

RRT

rapidly exploring random tree

SDM

shape deposition manufacturing

SEA

series elastic actuator

SURF

robust feature

TRIC

task space retrieval using inverse optimal control

VSA

variable stiffness actuator

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Robotics and Biology LaboratoryTechnical University BerlinBerlinGermany
  2. 2.Department of Transdisciplinary StudiesSeoul National UniversitySuwonKorea
  3. 3.Machine Learning and Robotics LabUniversity of StuttgartStuttgartGermany

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