A robot that peels vegetables in the same way that people do demonstrates a level of dexterity that could help move delicate objects along a manufacturing line. Prototype robots are often tasked with peeling vegetables to test their ability to carefully handle awkward objects. But these challenges are usually simplified, such as the vegetable being fixed in place, or only testing single fruits or vegetables, like peeling a banana. How this moment for AI will change society forever (and how it won't) Now, Pulkit Agrawal at the Massachusetts Institute of Technology and his colleagues have developed a robotic system that can rotate different types of fruit and vegetable using its fingers on one hand, while the other arm is made to peel. "These additional steps of doing rotation are something which is very straightforward to humans, we don't even think about it," says Agrawal. "But for a robot, this becomes challenging." First, the robot was taught in a simulated environment, receiving an algorithmic reward for a proper rotation and a punishment if it rotated the wrong way or not at all.

Congratulations to Tao Chen, Jie Xu and Pulkit Agrawal who have won the CoRL 2021 best paper award! Their work, A system for general in-hand object re-orientation, was highly praised by the judging committee who commented that "the sheer scope and variation across objects tested with this method, and the range of different policy architectures and approaches tested makes this paper extremely thorough in its analysis of this reorientation task". Below, the authors tell us more about their work, the methodology, and what they are planning next. We present a system for reorienting novel objects using an anthropomorphic robotic hand with any configuration, with the hand facing both upwards and downwards. We demonstrate the capability of reorienting over 2000 geometrically different objects in both cases.

MIT researchers have developed a system that improves the speed and agility of legged robots as they jump across gaps in the terrain. The movement may look effortless, but getting a robot to move this way is an altogether different prospect. In recent years, four-legged robots inspired by the movement of cheetahs and other animals have made great leaps forward, yet they still lag behind their mammalian counterparts when it comes to traveling across a landscape with rapid elevation changes. "In those settings, you need to use vision in order to avoid failure. For example, stepping in a gap is difficult to avoid if you can't see it. Although there are some existing methods for incorporating vision into legged locomotion, most of them aren't really suitable for use with emerging agile robotic systems," says Gabriel Margolis, a PhD student in the lab of Pulkit Agrawal, professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.