Controlling behavior in animals and robots

Summary

Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and computational exercises.

Content

Keywords

• behavior
• neuroscience
• neural networks
• flies
• robots
• embodiment
• sensing
• locomotion
• navigation

Learning Prerequisites

Required courses

Python programming

Recommended courses

Neuroscience

Important concepts to start the course

Neuroscience

Robotics

Programming

Learning Outcomes

By the end of the course, the student must be able to:

• Recall neurobiological and robotic algorithms for behavior
• Draw network models capable of carrying out simple behaviors
• Implement computational models to execute specific behaviors
• Hypothesize how changes in morphology, mechanical properties, or underlying algorithms would influence behaviors expressed
• Recall
• Draw
• Implement
• Hypothesize

Transversal skills

• Identify the different roles that are involved in well-functioning teams and assume different roles, including leadership roles.
• Give feedback (critique) in an appropriate fashion.
• Make an oral presentation.
• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Use both general and domain specific IT resources and tools
• Access and evaluate appropriate sources of information.
• Summarize an article or a technical report.

Teaching methods

For the first half of the semester:
Lectures per week, including a lecture by the professor as well as an oral presentation and guided discussion of primary scientific literature by a student group.
Exercises per week, including a guided programming-based simulation of behavioral control algorithms (problem solving, model building, project execution and presentation)
This first half of the course is consolidated and evaluated by a midterm exam.
For the second half of the semester: A group-based miniproject that is guided through lectures on data analysis and finalized through the submission of a Project Report and Presentation.

Expected student activities

Students are expected to attend lectures, actively engage in exercises, summarize and present a scientific study, participate in group discussions, and perform a miniproject in a group.

Assessment methods

Continued assessment during the semester.

Supervision

Resources

Moodle Link

• https://go.epfl.ch/BIOENG-456