Mobility and Manipulation

  • Oliver BrockEmail author
  • Jaeheung Park
  • Marc Toussaint
Part of the Springer Handbooks book series (SHB)


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.


Motion Planning Mobile Manipulator Motion Generation Mobile Platform Task Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





conditional random field


dynamic movement primitive


degree of freedom


Gaussian process


hidden Markov model


histogram of oriented features


Jet Propulsion Laboratory


k-nearest neighbor


normal distributions transform


occupancy map


partially observable Markov decision process


probabilistic roadmap


rapidly exploring random tree


shape deposition manufacturing


series elastic actuator


robust feature


task space retrieval using inverse optimal control


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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