Chances are that you’ve never given much thought to how insects walk, or what combination of leg movements–or gaits–is most stable or fastest, but, if like a group of scientists from Ramdya, Floreano and Ijspeert labs, NCCR Robotics, you are trying to create fast and robust robots, taking inspiration some of nature’s most agile movers might give you just the inspiration you need.
Felix Von Drigalski, of the Nara Institute of Science and Technology, introduces a versatile, open-source, two-finger gripper for textile manipulation that can sustain significant pushing loads in order to perform tucking tasks, using active perception.
Robotics, by definition, has been a discipline to aid other fields, such as manufacturing and space exploration. Over the past decade, it has become increasingly important in life sciences; a field that has been transformed by the convergence of insights and approaches from distinct scientific and technological disciplines. Robotics can help automate numerous processes — including repetitive tasks used in drug discovery, in vitro fertilization — and in lab bench work, such as analytical testing and preparation of chemical agents.
In our recent paper in Science Robotics, we show how robotics in the life sciences can also enable scientists to study and interrogate biological processes at the microscale in a dynamic and adaptive manner.
Engineers at MIT have fabricated transparent, gel-based robots that move when water is pumped in and out of them. The bots can perform a number of fast, forceful tasks, including kicking a ball underwater, and grabbing and releasing a live fish.
TERESA is a 3-year research project funded by the European Union and carried out by six institutions from four European countries. Its goal is to develop a new socially intelligent semi-autonomous telepresence system.
Brad Knox talks bots_alive and a new form of character AI. Much like motion capture for scripted animation, this new technique may revolutionize how interactive characters are created, through observation of authentic human-generated behavior.
The age of big data has seen a host of newtechniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.
That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.
So – you’ve built a robot arm. Now you’ve got to figure out how to control the thing. This was the situation I found myself in a few months ago, during my Masters project, and it’s a problem common to any robotic application: you want to put the end (specifically, the “end effector”) of your robot arm in a certain place, and to do that you have to figure out a valid pose for the arm which achieves that. This problem is called inverse kinematics (IK), and it’s one of the key problems in robotics.