Humanoid Robotics (RO5300) SS2019

[Links to previous courses: SS2018]

During the summer semester Prof. Dr. Elmar Rueckert is teaching the course Humanoid Robotics (RO5300). In this course he discusses the key components of one of the most complex autonomous systems. These topics are

  1. Kinematics, Dynamics & Simulation
  2. Representations of Skills & Imitation Learning
  3. Feedback Control, Priorities & Torque Control
  4. Planning & Cognitive Reasoning
  5. Reinforcement Learning & Policy Search

Ask questions at our course related Q&A page here.

Follow this link to register for the course:

Location & Times

This course provides a unique overview over central topics in robotics. A particular focus is put in the dependencies and interaction among the components in the control loop illustrated in the image above. These interactions are discussed in the context of state of the art methods including dynamical systems movement primitives, gradient based policy search methods or probabilisitic inference for planning algorithms.

In sum,  the lecture provides a structured and well motivated overview over modern techniques and tools which enable the students to define reward functions, implement robot controller and interaction software and to apply and extend state of the art reinforcement learning and planning approaches.

No special knowledge is required beforehand. All concepts and theories will be developed during the lectures or the tutorials.

The students will also experiment with state of the art machine learning methods and robotic simulation tools in accompanying exercises. Hands on tutorials on programming with Matlab and the simulation tool V-Rep complement the course content.

Course dates & materials (tentative slides, last update Feb. 15th, 2019)

Dates Chapter (0 to V)TopicsLinks
02.04VOAn Introduction to Humanoid RoboticsSlides
03.04UE0 Basics Matrix, Vectors, Inv. kinematics, gradient desc.
09.04VOI Kinematics, Dynamics & SimulationClassical forward and inverse kinematics
10.04VOI Kinematics, Dynamics & SimulationForward & inverse dynamics and numerical integration methods
16.04UE0 Basics Mechanical & dynamical systems
17.04UE0 Basics Differential equations & numerical solutions
24.04UE0 Basics V-Rep simulation env.
31.04UEAssignment IInv. kinematics
07.05VOII Representations of Skills & Imitation LearningThe movement primitive concept & imitation learning
08.05UE0 Basics Statistics, Bayes, Gaussian distributions
14.05VOII Representations of Skills & Imitation LearningDynamical systems movement primitives and outlook of probabilistic and neural primitives
15.05UEAssignment IIDynamical systems movement primitives
21.05VOIII Feedback Control, Priorities & Torque ControlClassical PID Control & rigid body dynamics
22.05UEI to IIRecap and Q&As
28.05VOIII Feedback Control, Priorities & Torque ControlOptimal Feedback Control with LQRs
29.05UEAssignment IIIPID and LQR control
04.06VOIV Planning & Cognitive ReasoningSampling based planning, RRT
11.06VOIV Planning & Cognitive ReasoningOptimal planning in Markov decision problems
12.06UEI to IIIRecap and Q&As
18.06VOV Reinforcement Learning & Policy SearchMarkov reward & decision processes, value iteration, Q-learning
19.06UEAssignment IVPlanning, RRT
09.07VOSummary & potential exam questions
10.07UEAssignment IV Presentations
15.07Tentative date of the written exam
07.10Tentative date of the written exam

Materials for the exercise

For simulating robot manipulation tasks we will use the simulator V-REP. For research and for teaching a free eduction version can be found here. To experiment with state of the art robot control and learning methods Mathworks’ MATLAB will be used. If you do not have it installed yet, please follow the instructions of our IT-Service Center.

Matlab Files shown during the Tutorial can be found here.