Spatial Computing to the Workplace

Looking Glass, an XR display manufacturer, recently revealed that new spatial display products will be available for enterprise audiences this month. The new product rollout includes devices that aim to enhance the firm’s portfolio of 3D monitors. These monitors allow users to experience a spatial computing desktop experience with additional integrated technologies. Looking Glass’ new displays are a great example of how XR and spatial computing technologies are present in various end devices, not just AR/VR/MR headsets and will distribute 16-inch and 32-inch XR displays.

The firm is also rolling out the Looking Glass Go, a more straightforward consumer display device. The new Looking Glass displays allow users to work on 3D-based workflows using interactive real-time models with which users can interact hands-on as an AR visualisation via tracking cameras. The displays are suited for use cases such as product design procedures. The 16” and 32” displays allow professionals to collaborate over a single spatial display. Meanwhile, the Looking Glass Go product will enable consumers to turn 2D images into 3D spatial visualisations.

More information:

https://www.xrtoday.com/augmented-reality/new-3d-monitors-brings-spatial-computing-to-the-workplace-this-month/

Robotic Table Tennis Player

Researchers from Google’s DeepMind Robotics team have effectively developed a solidly amateur human-level player when pitted against a human component. During testing, the table tennis bot was able to beat all of the beginner-level players it faced. With intermediate players, the robot won 55% of matches. It’s not ready to take on pros, however. The robot lost every time it faced an advanced player. All told, the system won 45% of the 29 games it played.

The system’s biggest shortcoming is its ability to react to fast balls. DeepMind suggests the key reasons for this are system latency, mandatory resets between shots and a lack of useful data. Other exploitable issues with the system are high and low balls, backhand and the ability to read the spin on an incoming ball. As far as how such research could affect robotics beyond the very limited usefulness of table tennis, its ability to adapt its strategy in real time.

More information:

https://techcrunch.com/2024/08/08/google-deepmind-develops-a-solidly-amateur-table-tennis-robot/

Damaged Robot Swims Like Injured Fish

Researchers investigated how robotic systems can adapt their propulsion mechanisms in response to damage, drawing inspiration from how fish and insects adjust their movement when injured. They tested a flapping robot in a tank of oil, which was chosen for its superior signal-to-noise ratio compared to water. After amputating a portion of the robot’s flapper, they employed machine learning to help the robot adapt its propulsion mechanism.

Without help, the robot’s damaged flapper would have made it unable to move. However, by incorporating bioinspired adaptation techniques, the robot was programmed to experiment with various stroke mechanics. The system ran multiple trials to determine which mechanics allowed the robot to move efficiently despite the damage. Through machine learning algorithms, the robot refined its movement patterns, achieving effective propulsion even with 50 percent of its flapper removed.

More information:

https://interestingengineering.com/science/robot-adapts-damage-bioinspired-techniques

Robot Peels a Squash

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. Researchers at the Massachusetts Institute of Technology 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. 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. Next, the robot was tested under real-world conditions by tasking it with peeling fruits and vegetables such as a pumpkin, radish and papaya.

It used one hand to rotate the produce, using feedback from touch sensors, while a human-controlled robot arm did the peeling. The algorithm struggles with smaller, more awkwardly shaped vegetables, such as ginger, but the team hopes to expand its capabilities. Grasping and reorienting objects are challenging tasks for any robot, and the speed and firm grip of this one is impressive. It could be useful in factories where objects must be moved from one machine to another with the correct orientation. However, it is unlikely to be used in an industrial setting for peeling vegetables because other approaches already exist, such as automatic potato peelers.

More information:

https://www.newscientist.com/article/2440687-watch-a-robot-peel-a-squash-with-human-like-dexterity/