The Giant Gundam in Yokohama is actually way cooler than I thought it was going to be.
A new 3D-printing method will make it easier to manufacture and control the shape of soft robots, artificial muscles and wearable devices. Researchers at UC San Diego show that by controlling the printing temperature of liquid crystal elastomer, or LCE, they can control the material’s degree of stiffness and ability to contract—also known as degree of actuation. What’s more, they are able to change the stiffness of different areas in the same material by exposing it to heat.
[ UCSD ]
This is the first successful reactive stepping test on our new torque-controlled biped robot named Bolt. The robot has 3 active degrees of freedom per leg and one passive joint in ankle. Since there is no active joint in ankle, the robot only relies on step location and timing adaptation to stabilize its motion. Not only can the robot perform stepping without active ankles, but it is also capable of rejecting external disturbances as we showed in this video.
[ ODRI ]
The curling robot “Curly” is the first AI-based robot to demonstrate competitive curling skills in an icy real environment with its high uncertainties. Scientists from seven different Korean research institutions including Prof. Klaus-Robert Müller, head of the machine-learning group at TU Berlin and guest professor at Korea University, have developed an AI-based curling robot.
[ TU Berlin ]
MoonRanger, a small robotic rover being developed by Carnegie Mellon University and its spinoff Astrobotic, has completed its preliminary design review in preparation for a 2022 mission to search for signs of water at the moon’s south pole. Red Whittaker explains why the new MoonRanger Lunar Explorer design is innovative and different from prior planetary rovers.
[ CMU ]
Cobalt’s security robot can now navigate unmodified elevators, which is an impressive feat.
[ Cobalt ]
OrionStar, the robotics company invested in by Cheetah Mobile, announced the Robotic Tea Master. Incorporating 3,000 hours of AI learning, 30,000 hours of robotic arm testing and machine vision training, the Robotic Tea Master can perform complex brewing techniques, such as curves and spirals, with millimeter-level stability and accuracy (reset error ≤ 0.1mm).
It also makes coffee, but we don’t care about that.
[ Cheetah Mobile ]
DARPA OFFensive Swarm-Enabled Tactics (OFFSET) researchers recently tested swarms of autonomous air and ground vehicles at the Leschi Town Combined Arms Collective Training Facility (CACTF), located at Joint Base Lewis-McChord (JBLM) in Washington. The Leschi Town field experiment is the fourth of six planned experiments for the OFFSET program, which seeks to develop large-scale teams of collaborative autonomous systems capable of supporting ground forces operating in urban environments.
[ DARPA ]
Here are some highlights from Team Explorer’s SubT Urban competition back in February.
[ Team Explorer ]
Researchers with the Skoltech Intelligent Space Robotics Laboratory have developed a system that allows easy interaction with a micro-quadcopter with LEDs that can be used for light-painting. The researchers used a 92x92x29 mm Crazyflie 2.0 quadrotor that weighs just 27 grams, equipped with a light reflector and an array of controllable RGB LEDs. The control system consists of a glove equipped with an inertial measurement unit (IMU; an electronic device that tracks the movement of a user’s hand), and a base station that runs a machine learning algorithm.
[ Skoltech ]
“DeKonBot” is the prototype of a cleaning and disinfection robot for potentially contaminated surfaces in buildings such as door handles, light switches or elevator buttons. While other cleaning robots often spray the cleaning agents over a large area, DeKonBot autonomously identifies the surface to be cleaned.
[ Fraunhofer IPA ]
On Oct. 20, the OSIRIS-REx mission will perform the first attempt of its Touch-And-Go (TAG) sample collection event. Not only will the spacecraft navigate to the surface using innovative navigation techniques, but it could also collect the largest sample since the Apollo missions.
[ NASA ]
With all the robotics research that seems to happen in places where snow is more of an occasional novelty or annoyance, it’s good to see NORLAB taking things more seriously
[ NORLAB ]
Telexistence’s Model-T robot works very slowly, but very safely, restocking shelves.
Roboy 3.0 will be unveiled next month!
[ Roboy ]
KUKA ready2_educate is your training cell for hands-on education in robotics. It is especially aimed at schools, universities and company training facilities. The training cell is a complete starter package and your perfect partner for entry into robotics.
[ KUKA ]
A UPenn GRASP Lab Special Seminar on Data Driven Perception for Autonomy, presented by Dapo Afolabi from UC Berkeley.
Perception systems form a crucial part of autonomous and artificial intelligence systems since they convert data about the relationship between an autonomous system and its environment into meaningful information. Perception systems can be difficult to build since they may involve modeling complex physical systems or other autonomous agents. In such scenarios, data driven models may be used to augment physics based models for perception. In this talk, I will present work making use of data driven models for perception tasks, highlighting the benefit of such approaches for autonomous systems.
[ GRASP Lab ]
A Maryland Robotics Center Special Robotics Seminar on Underwater Autonomy, presented by Ioannis Rekleitis from the University of South Carolina.
This talk presents an overview of algorithmic problems related to marine robotics, with a particular focus on increasing the autonomy of robotic systems in challenging environments. I will talk about vision-based state estimation and mapping of underwater caves. An application of monitoring coral reefs is going to be discussed. I will also talk about several vehicles used at the University of South Carolina such as drifters, underwater, and surface vehicles. In addition, a short overview of the current projects will be discussed. The work that I will present has a strong algorithmic flavour, while it is validated in real hardware. Experimental results from several testing campaigns will be presented.
[ MRC ]
This week’s CMU RI Seminar comes from Scott Niekum at UT Austin, on Scaling Probabilistically Safe Learning to Robotics.
Before learning robots can be deployed in the real world, it is critical that probabilistic guarantees can be made about the safety and performance of such systems. This talk focuses on new developments in three key areas for scaling safe learning to robotics: (1) a theory of safe imitation learning; (2) scalable reward inference in the absence of models; (3) efficient off-policy policy evaluation. The proposed algorithms offer a blend of safety and practicality, making a significant step towards safe robot learning with modest amounts of real-world data.
[ CMU RI ]
Source: IEEE Spectrum