Challenges of Autonomy: The Video

Autonomous systems are a key focal point in today’s cognitive systems research, particularly through high profile association with self-driving cars. But vehicles are only a part of the story. Autonomy and partial autonomy are also important to a growing array of industrial and consumer robots, drones, and new devices from the IoT.

Autonomy is a unique problem involving an array of technologies, and it has implications for technological growth, the economy, and for human society. Autonomous systems need to operate safely in cooperation with people; they require extensive sensors, composite intelligence, and Fog Computing (device level processing linked to cloud level processing and coordination).

In the following videos, we look at aspects of autonomy. The video descriptions are drawn from the sources.

Intelligent Autonomous Systems (Distinctive Voices)

This talk describes the current research path towards intelligent, semi-autonomous systems, where both humans and automation tightly interact, and together, accomplish tasks such as searching for survivors of a hurricane using a team of UAVs with sensors with highly efficient interaction. This talk is describes the current state of the art in:

1. Intelligent robotic (only) systems;

2. Modeling human decisions;

3. Semi-autonomous systems, with a focus on information exchange, and command and control.

By Mark Campbell, the S.C. Thomas Sze Director of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. Oct 18, 2012

Autonomous Technology and the Greater Human Good – Winter Intelligence (Winter Intelligence Conference, Oxford)

Next generation technologies will make at least some of their decisions autonomously. Self-driving vehicles, rapid financial transactions, military drones, and many other applications will drive the creation of autonomous systems. If implemented well, they have the potential to create enormous wealth and productivity. But if given goals that are too simplistic, autonomous systems can be dangerous.

By Stephen Omohundro, a scientist researching Hamiltonian physics, dynamical systems, programming languages, machine learning, machine vision, and the social implications of artificial intelligence. Apr 14, 2013

Developing Trust in Autonomous Robots (Carnegie Mellon University Robotics Institute)

By Michael Wagner Senior NREC Commercialization Specialist, RI, NREC. Feb 20, 2015

Intelligent Systems – Computers Learn to Understand the World (MaxPlanckSociety)

Autonomous systems such as robots or self-driving cars are expected to play an increasingly important role in future. Initially, however, they must learn how to safely negotiate their environments. To this end, Michael Black teaches them to recognize people: a technology that will enable even more amazing applications.

By Michael Black, Max Planck Institute. Aug 8, 2016

The Future of Autonomous Systems: a Computational Perspective (Stanford ICMEStudio)

Mobile autonomous systems are poised to transform several sectors, from transportation and logistics all the way to space exploration. This talk briefly reviews major computational challenges in endowing autonomous systems with fast and reliable decision-making capabilities, and discusses recent advances made at the Stanford Autonomous Systems Laboratory.

By Marco Pavone, Assistant Professor of Aeronautics and Astronautics, Stanford. May 27, 2016


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