Monday morning, some of the world's top minds in robotics and machine learning were due to convene for a virtual, invite-only research workshop hosted by Google. Two academics invited didn't log on as scheduled: They withdrew to protest Google's treatment of two women who've said they were unjustly fired from the company's artificial intelligence research division. A third academic who previously received funding from Google took his own stand, saying he would no longer apply for its support. Although small in scale, the boycott illustrates some of the damage to Google's reputation from the acrimonious departures of Timnit Gebru and Margaret Mitchell, coleaders of a team working to make AI systems more ethical. The controversy has drawn new attention to the influence of tech companies on AI research, and has led researchers inside and outside of Google to ask whether it was distorting research into AI's impact on society.

We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric. We incorporate task-critical information, that is only available at runtime, into the strategy synthesis in order to improve performance. Existing approaches to utilising such time-varying information require online re-synthesis, which is not computationally feasible in real-time applications. In this paper, we pre-synthesize a set of strategies corresponding to candidate instantiations (pre-specified representative information scenarios). We then propose a novel switching mechanism to dynamically switch between the strategies at runtime while guaranteeing all safety and liveness goals are met. We also characterize bounds on the performance suboptimality. We demonstrate our approach on two examples - robotic motion planning where the likelihood of the position of the robot's goal is updated in real-time, and an air traffic management problem for urban air mobility.

A shape-shifting robot that can autonomously reconfigure itself into a variety of different shapes has been developed by scientists. The device can perceive its own surroundings, make decisions and autonomously assume different shapes, they say. That means the shape-shifting machine can easily switch between a delivery drone and an arm that lifts and moves objects. It is hoped that similar gadgets will one day be used in search and rescue operations and to explore distant planets. The shape-shifting robot is composed of wheeled, cube-shaped modules that can detach and reattach to form new shapes.

When you've got a hammer, everything looks like a nail, but the world starts to look more interesting if your hammer can change shape. For the builders of a class of robots called modular self-reconfigurable robots (MSRR), shape-shifting is the first step toward endowing robots with an animal-like adaptability to unknown situations. "The question of autonomy becomes more complicated, more interesting," when robots can change themselves to meet changing circumstances, said roboticist Hadas Kress-Gazit of Cornell University. The key to achieving adaptability for robots rests in centralized sensory processing, environmental perception, and decision-making software, Kress-Gazit and colleagues report this week in a new paper in Science Robotics. The authors claim their new work represents the first time a modular robot has autonomously solved problems by reconfiguring in response to a changing environment.

This paper shows an example application of the LTLMoP mission planning toolkit, in which an Aldebaran Nao and an iRobot Create each play a very basic game of hide-and-seek.