Humanoid Robotics

Many kids join us to fulfil their childhood dream of making a robot that looks exactly the same as any science fiction movie, and therefore, to advertise we have to run a course that promises exactly the same thing. Being an IT institute, we primarily look at the software side of it, and making education affordable, focus on the simulation aspects.

The course on humanoid robotics takes you through a journey covering all software components that power modern robots. Even though the students often get lost in this journey, we make sure that the human fate is never witnessed by the robots by focused on Simultaneous Localization and Mapping concepts, through which robots always get a map and their location. The students often re-visit the rationality of their decision of choosing this elective, however, the course ensures that the robots make the best decisions always for navigation through a mixture of planning techniques and execution through controls. Because we deal with students, we know the meaning of every gesture that the students make during the class, and teach the students how to impart the same skill set into robots. To add some fun, the course also does window shopping of different robots, sensors and cool gadgets.


S. No. Topic Details
1. Introduction to Mobile Robotics Hardware, Software, Vision, Localization, Mapping, Planning, Control, HRI, real life examples, and related topics
2. Sensing Different kinds of sensors used in robotics, robotics laboratory visit to see different robots and their sensors and actuators
3. Filtering Basics, Kalman Filter, Extended Kalman Filter, Particle Filter
4. Localization Robot Motion Models, Robot Observation Models, Correspondence
5. Mapping Occupancy Grid Mapping, Beliefs, Fusiosn from different sensors
6. Simultaneous Localization and Mapping (SLAM) EKF-SLAM, Visual Odometry, Visual SLAM, Visual Place Recognition, Learning in SLAM
7. Cognitive robotics Reactive approach, Subsumption Architecture, Deliberative Approach, Hybrid Deliberative/Reactive approach
8. Multimodal Human-Robot Interaction Computer Vision Basics, Features, Feature Matching, Segmentation, Learning, Speech Recognition, Gesture Recognition, Hidden Markov Models, Fusion of different modes
9. Roadmap Approaches Roadmaps, Visibility graphs, Deformation Retracts, Voronoi, Generalized Voronoi Diagram, Generalized Voronoi Graph
10. Control Basics of control theory, PID control, control of mobile robots
11. Fundamentals of Biped Locomotion Control Stability, ZMP, gait cycle
12. Full Body Humanoid Planning Collission Detection, Configuration Space, Probabilistic Roadmap, Topology, Manifolds

Lab Syllabus

Simulation of Robots including motion models, observation models and noise
Localization using simulated robots
SLAM using simulated robots
SLAM using RTABMAP or a similar library

Individual Projects based on available libraries


The students go through the course on Artificial Life Simulation and therefore those topics are assumed to be known. Because not all students have done courses on Computer Vision, some elements are briefly repeated, but a knowledge of vision through taught courses or projects is extremely helpful. Similarly, knowledge of searching (Artificial Intelligence) can be very useful.

Text/Reference Material

  • Main Text Book 1: S. Thrun, W. Burgard, D. Fox, R.C. Arkin (2005) Probabilistic Robotics, MIT Press, Cambridge, MA.
  • Main Text Book 2: R. R. Murphy (2000) Introduction to AI Robotics, MIT Press, Cambridge, MA.
  • Research Papers are given for the other topics not covered in either of the books.

Myself on:

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Dr. Rahul Kala
Assistant Professor,
IIIT Allahabad,

Phone: +91 532 299 2117
Mobile: +91 7054 292 063