• Overview

    The course provides an overview of mathematical models and algorithms behind state-of-the-art robotic manipulation. The covered viewpoints include grasping. motion planning, motion control, control in contact and redundancy, and learning manipulation skills. 

    After completing the course, a student can: (i) explain main concepts related to robotic manipulation; (ii) read scientific literature in robotics to choose approaches for a particular problem; (iii) implement state-of-the-art algorithms.

  • Concept

    Lectures will be places of discussion where the current topic is summarized by the lecturer and discussed among all present. The students are expected to prepare by reading given material in advance prior to each lecture.


    Course lectures will be given during third and fourth period. For lecture times and locations, please check Oodi.

    Readings / Videos

    For each lecture starting from the second one, there will be reading materials that the students should study before attending the lecture.

    Introduction, Jan 7, no readings
    Motion planning, Jan 14, Lynch & Park ch. 5-5.1.4
    Perception for manipulation, Jan 21, Lynch & Park ch. 10
    Motion control, Jan 28, Lynch & Park ch. 11-11.3.2
    Control in contact, Feb 4, Lynch & Park ch 11.5-11.6
    Manipulation, Feb 11, Lynch & Park ch 12-12.1.3
    Friction and grasping, Feb 18, Lynch & Park, Chapter 12.2-12.2.2
    Closed kinematic chains, Feb 25, Lynch & Park, Chapter 7-7.1.3
    Redundancy, Mar 4, Chiaverini et al., “Redundant robots”, Springer Handbook of Robotics, 2nd ed., ch. 10-10.2.2.
    Learning/modeling of manipulation skills, Mar 18, Peters et al., “Robot Learning”, in Springer Handbook of Robotics, 2nd ed., secs. 15-15.1.2, 15.4
                             and Brock et al., “Mobility and Manipulation”, in Springer Handbook of Robotics, 2nd ed., secs. 40, 40.4, 40.4.2-40.4.3
    Task and motion planning, Mar 25, Springer Handbook of Robotics, 2nd ed., secs. 14.3-14.3.2, 36.3-36.3.3
  • The course exercise assignments are handled through Aalto gitlab. All instructions will be posted there. Some material will also be available below.

    The course gitlab repository is reachable at https://version.aalto.fi/gitlab/robotic_manipulation

    Grading and evaluation

    To pass, 50% of maximum total grade must be achieved.


    • Quizz-assignments 25%
    • Exercise-assignments 75%
    Individual Exercise-assignment points:
    1. 5 points
    2. 15 points
    3. 15 points
    4. 15 (+ 4.5) points (if you do the bonus task)
    5. 15 points
    6. 15 (+ 4.5) points (if you do the bonus task)
    7. 20 points
    Total exercise points are 100 (+ 9) points.

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