Cognitive Processing

, Volume 16, Supplement 1, pp 293–297 | Cite as

Dual filtering in operational and joint spaces for reaching and grasping

  • Léo LopezEmail author
  • Jean-Charles Quinton
  • Youcef Mezouar
Short Report


To study human movement generation, as well as to develop efficient control algorithms for humanoid or dexterous manipulation robots, overcoming the limits and drawbacks of inverse-kinematics-based methods is needed. Adequate methods must deal with high dimensionality, uncertainty, and must perform in real time (constraints shared by robots and humans). This paper introduces a Bayesian filtering method, hierarchically applied in the operational and joint spaces to break down the complexity of the problem. The method is validated in simulation on a robotic arm in a cluttered environment, with up to 51 degrees of freedom.


Bayesian filtering Reaching Operational space  Joint space Grasping 


  1. Argall BD, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstration. Robot Auton Syst 57(5):469–483CrossRefGoogle Scholar
  2. Deneve S, Duhamel J-R, Pouget A (2007) Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters. J Neurosci 27(21):5744–5756CrossRefPubMedGoogle Scholar
  3. Diankov R, Kuffner J (2008) Openrave: a planning architecture for autonomous robotics. Robotics Institute, Pittsburgh, PA, Technical report CMU-RI-TR-08-34, 79Google Scholar
  4. Doucet A, De Freitas N, Gordon N (2001) An introduction to sequential Monte Carlo methods. In: Sequential Monte Carlo methods in practice. Springer, New York, pp 3–14Google Scholar
  5. Fu KS, Gonzalez R, Lee CG (1987) Robotics: control sensing vision and intelligence. McGraw-Hill, New YorkGoogle Scholar
  6. Korein JU, Badler NI (1982) Techniques for generating the goal-directed motion of articulated structures. IEEE Comput Graph Appl 2(9):71–81CrossRefGoogle Scholar
  7. Lee CG (1982) Robot arm kinematics, dynamics, and control. Computer 15(12):62–80CrossRefGoogle Scholar
  8. Sturm J, Plagemann C, Burgard W (2009) Body schema learning for robotic manipulators from visual self-perception. J Physiol Paris 103(3):220–231CrossRefPubMedGoogle Scholar

Copyright information

© Marta Olivetti Belardinelli and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Léo Lopez
    • 1
    Email author
  • Jean-Charles Quinton
    • 1
    • 2
  • Youcef Mezouar
    • 2
    • 3
  1. 1.Pascal InstituteClermont UniversityClermont-FerrandFrance
  2. 2.Pascal InstituteCNRS (UMR 6602)AubiereFrance
  3. 3.Pascal InstituteIFMAAubiereFrance

Personalised recommendations