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Robotics and Autonomous Systems

  • Course level: Intermediate

Description

Robotics and Autonomous Systems

Robots must be able to accomplish tasks in a wide variety of scenarios to be effective in unconstrained environments – they must be able to generalize. For a variety of challenges, we’ve seen excellent outcomes from machine learning algorithms that generalize to large real-world datasets. As a result, machine learning gives robots a potent tool to accomplish the same.

Machine learning algorithms for robotics, on the other hand, frequently generalize narrowly inside a specific laboratory context. In this session, I’ll talk about the problems that robots confront in comparison to traditional machine learning problem settings, and how we might rethink both our robot learning algorithms and our data sources to enable robots to generalize broadly spans jobs, settings, and robot platforms

This course is intended for the following individuals:

Robotics enthusiasts, as well as anyone interested in learning more about robotic, are welcome to attend.

What Will I Learn?

  • Knowledge of robotics systems in general

Topics for this course

15 Lessons

Robotics and Autonomous Systems

Seminar – Designing bioinspired aerial robots with feathered morphing wings00:00:00
Seminar – Toward robust manipulation in complex environments00:00:00
Seminar – Safe and Robust Perception-Based Control00:00:00
Seminar – Safety-Critical Control of Dynamic Robots00:00:00
Seminar – Designing More Effective Remote Presence Systems for Human Connection00:00:00
Seminar – Robotic Autonomy and Perception in Challenging Environments00:00:00
Seminar – Distributed Perception and Learning Between Robots and the Cloud00:00:00
Seminar – The Next Generation of Robot Learning00:00:00
Seminar – Hands in the Real World: Grasping Outside the Lab00:00:00
Seminar – Model Predictive Control of Hybrid Dynamical Systems00:00:00
Seminar – Learning and Predictions in Autonomous Systems00:00:00
Seminar – Field-hardened Robotic Autonomy00:00:00
Seminar – Self-Supervised Pseudo-Lidar Networks00:00:00
Seminar – Modeling and Control for Robotic Assistants00:00:00
Seminar – Bridging model-based and data-driven reasoning for safe human-centered robotics00:00:00
Free

Enrolment validity: Lifetime

Requirements

  • Graduate Level
  • Basic Knowledge of Robotics
  • Electrical Engineer, Computer Engineer, Communication Engineer