Episode 155: Making aerial robots smarter - Melissa Greeff

Robot Talk · 8 May 2026 · 24m · Episode 155

What this episode covers

Melissa Greeff, an Assistant Professor at Queen's University and leader of the Robora Lab, discusses her research on autonomous aerial systems, including vision-based navigation and safe learning-based control. She details projects involving drones landing on surface vessels for maritime operations, tiny drones using custom optimization solvers for agile flight, and multi-robot teams where aerial vehicles teach ground robots routes. Greeff emphasizes the challenges of bridging perception and control, limited onboard compute, and the gap between academic prototypes and persistent industrial autonomy.

Other shows on the same ground, closest first.

  • The Next Byte Podcast · 25m

    49. What Robots Can Learn from Starfish and Falcons

    Hosts Daniel and Forbode discuss biomimicry in robotics, starting with a video of Grant Imahara visiting Carnegie Mellon University to explore bio-inspired robots like the snake robot. They then detail a starfish larva-inspired micro-robot developed at ETH Zurich that uses ultrasonic waves for targeted drug delivery, and a Peregrine Falcon-inspired drone landing system called 'Snag' developed by Stanford graduates at the University of Groningen.

  • Embedded.fm · 50m

    234: The Good Word About AI

    Dusty Franklin, a developer evangelist at NVIDIA, discusses the Jetson TX2, a low-power embedded module designed to bring AI and computer vision to edge devices like robots and drones. He explains how the platform's GPU enables parallel processing for tasks such as autonomous navigation, object detection, and reinforcement learning, often using simulation environments like ROS Gazebo to train agents safely. Franklin also details the 'Two Days to a Demo' tutorial, which guides developers through deploying and retraining neural networks using NVIDIA's TensorRT and DIGITS tools, and mentions the Redtail Project as an example of autonomous drone navigation.

  • EE Times Current · 48m

    Seeing Robotics and Machine Vision as Dynamical Systems

    Dr. Yulia Sandamirskaya, now leading the Neuromorphic Computing Group at Zurich University of Applied Sciences (ZHAW), discusses her work applying dynamical systems theory to robotics and machine vision. She details her previous role at Intel, where she utilized the Loihi neuromorphic platform to develop real-time robotic applications, including drone control and human-robot interaction. The episode also features commentary from Ralph Etienne-Cummings of Johns Hopkins University on the challenges of embodiment, active vision, and the need for heterogeneous hardware architectures.

  • Hackaday Podcast · 1h 04m

    Ep059: Hydraulic Rockets & Presses, Machine Vision Bounces & Stares, Smart Speakers Listen to You

    Hackaday editors Mike Szczys and Elliot Williams discuss several hardware hacks, including Joe Barnard’s silo-launched thrust-vectoring model rocket, which required solving pneumatic timing and sensor failures, and Graham Jessup’s 3D-printed robotic face with machine vision tracking. They also cover Daniel Riley’s solar-powered rover, which overcame steering and traction issues through iterative frame modifications, and a robotic ball bouncer using a parallel manipulator and OpenCV. The episode concludes with an analysis of smart speaker false activations and a discussion of Northrop Grumman’s Mission Extension Vehicle, a space tow truck designed to extend satellite life in geosynchronous orbit.

  • The Next Byte Podcast · 20m

    170. Robot That Drives Better Than The Average American

    Daniel and Forbode discuss research from Professor Sue Xiao’s Robotics Lab at George Mason University, which developed an autonomous robot capable of driving better than humans on variable terrain. The team instrumented an off-the-shelf robot with visual and inertial sensors to collect data on stability, training a machine learning model that reduced instability by 62% while sacrificing only 8.6% of speed. This technology, applicable to first responders and drones, allows robots to adapt their driving style to terrain changes like gravel or ice, improving safety for both autonomous and human-in-the-loop scenarios.

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by Robot Talk. The episode itself belongs to its publisher.