Robotics and Autonomous Systems

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About Course

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.

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What Will You Learn?

  • Knowledge of robotics systems in general

Course Content

Robotics and Autonomous Systems

  • Seminar – Designing bioinspired aerial robots with feathered morphing wings
  • Seminar – Toward robust manipulation in complex environments
  • Seminar – Safe and Robust Perception-Based Control
  • Seminar – Safety-Critical Control of Dynamic Robots
  • Seminar – Designing More Effective Remote Presence Systems for Human Connection
  • Seminar – Robotic Autonomy and Perception in Challenging Environments
  • Seminar – Distributed Perception and Learning Between Robots and the Cloud
  • Seminar – The Next Generation of Robot Learning
  • Seminar – Hands in the Real World: Grasping Outside the Lab
  • Seminar – Model Predictive Control of Hybrid Dynamical Systems
  • Seminar – Learning and Predictions in Autonomous Systems
  • Seminar – Field-hardened Robotic Autonomy
  • Seminar – Self-Supervised Pseudo-Lidar Networks
  • Seminar – Modeling and Control for Robotic Assistants
  • Seminar – Bridging model-based and data-driven reasoning for safe human-centered robotics

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