A novel system developed at MIT uses RFID tags to help robots home in on moving objects with unprecedented speed and accuracy. The system could enable greater collaboration and precision by robots working on packaging and assembly, and by swarms of drones carrying out search-and-rescue missions.

California has released the disengagement reports the law requires companies to file and it’s a lot of data. Also worth noting is Waymo’s own blog post on their report where they report their miles per disengagement has improved from 5,600 to 11,000.

In this episode, Audrow Nash interviews Amy Loutfi, a professor at Örebro University, about how semantic representations can be used to help robots reason about the world.  Loutfi discusses semantics in general, as well as how semantics have been used for a simulated quad rotor to do path planning within constraints.

By Rohin Shah and Dmitrii Krasheninnikov

It would be great if we could all have household robots do our chores for us. Chores are tasks that we want done to make our houses cater more to our preferences; they are a way in which we want our house to be different from the way it currently is. However, most “different” states are not very desirable:

Surely our robot wouldn’t be so dumb as to go around breaking stuff when we ask it to clean our house? Unfortunately, AI systems trained with reinforcement learning only optimize features specified in the reward function and are indifferent to anything we might’ve inadvertently left out. Generally, it is easy to get the reward wrong by forgetting to include preferences for things that should stay the same, since we are so used to having these preferences satisfied, and there are so many of them. Consider the room below, and imagine that we want a robot waiter that serves people at the dining table efficiently. We might implement this using a reward function that provides 1 reward whenever the robot serves a dish, and use discounting so that the robot is incentivized to be efficient. What could go wrong with such a reward function? How would we need to modify the reward function to take this into account? Take a minute to think about it.

This week Washington DC was abuzz with news that had nothing to do with the occupant of The While House. A group of progressive legislators, led by Alexandra Ocasio-Cortez, in the House of Representatives, introduced “The Green New Deal.” The resolution by the Intergovernmental Panel on Climate Change was in response to the alarming Fourth National Climate Assessment and aims to reduce global “greenhouse gas emissions from human sources of 40 to 60 percent from 2010 levels by 2030; and net-zero global emissions by 2050.” While the bill is largely targeting the transportation industry, many proponents suggest that it would be more impactful, and healthier, to curb America’s insatiable appetite for animal agriculture.

In this interview, Audrow Nash interviews Jaime Fernández Fisac, a PhD student at University of California, Berkeley, working with Professors Shankar Sastry, Claire Tomlin, and Anca Dragan. Fisac is interested in ensuring that autonomous systems such as self-driving cars, delivery drones, and home robots can operate and learn in the world—while satisfying safety constraints. Towards this goal, Fisac discusses different examples of his work with unmanned aerial vehicles and talks about safe robot learning in general; including, the curse of dimensionality and how it impacts control problems (including how some systems can be decomposed into simpler control problems), how simulation can be leveraged before trying learning on a physical robot, safe sets, and how a robot can modify its behavior based on how confident it is that its model is correct.

People’s interactions with machines, from robots that throw tantrums when they lose a colour-matching game against a human opponent to the bionic limbs that could give us extra abilities, are not just revealing more about how our brains are wired – they are also altering them.

Emily Cross is a professor of social robotics at the University of Glasgow in Scotland who is examining the nature of human-robot relationships and what they can tell us about human cognition.

My feet are aching, as usual, after 3 days on the CES show floor, and the question people always ask others there is “what have you seen that was interesting?”

I won’t say I didn’t see anything interesting, and I had a large number of rewarding conversations with all sorts of companies, making the trip very worthwhile, but I will say I saw less that was new and exciting than ever before. This may be a result of the show’s constant growth that meant in 3 days I still did not manage to get to 3 1/2 major rooms of the show, putting my focus on cars as I usually do.

By Rob Matheson

A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have “learned” from training examples that don’t match what’s actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots.