Picking up a can of soda is a simple task for humans, but it is a complex task for robots, which has to locate the object, deduce its shape, determine the right amount of strength to use, and grasp the object without letting it slip. Most of today's robots operate solely based on visual processing, which limits their capabilities. In order to perform more complex tasks, robots have to be equipped with an exceptional sense of touch and the ability to process sensory information quickly and intelligently.
A team of computer scientists and materials engineers from the National University of Singapore has recently demonstrated an approach to make robots smarter. They developed a sensory integrated artificial brain system that mimics biological neural networks, and runs on Intel's power-efficient neuromorphic processor, the Loihi chip. The novel system integrates artificial skin and vision sensors, equipping robots with an ability to draw accurate conclusions about the objects they are grasping based on the data captured by vision and touch sensors in real-time.
"The field of robotic manipulation has made great progress in recent years. However, fusing both vision and tactile information to provide a highly precise response in milliseconds remains a technology challenge," says Assistant Professor Benjamin Tee from the NUS Department of Materials Science and Engineering. "Our recent work combines our ultra-fast electronic skins and nervous systems with the latest innovations in vision sensing and AI for robots so that they can become smarter and more intuitive in physical interactions." Tee co-leads this project with Assistant Professor Harold Soh from the Department of Computer Science at the NUS School of Computing.
The researchers describe their work in "Event-Driven Visual-Tactile Sensing and Learning for Robots," presented this month at the Robotics: Science and Systems conference.
Enabling a human-like sense of touch in robotics could significantly improve current functionality, and even lead to new uses. For example, on a factory floor, robotic arms fitted with electronic skins could easily adapt to different items, using tactile sensing to identify and grip unfamiliar objects with the right amount of pressure to prevent slipping.
In their robotic system, the NUS team applied an advanced artificial skin known as Asynchronous Coded Electronic Skin (ACES) developed by Tee and his team in 2019. This sensor detects touches more than 1,000 times faster than the human sensory nervous system. It can also identify the shape, texture, and hardness of objects 10 times faster than the blink of an eye.
"Making an ultra-fast artificial skin sensor solves about half the puzzle of making robots smarter. They also need an artificial brain that can ultimately achieve perception and learning as another critical piece in the puzzle," says Tee, who is also from the NUS Institute for Health Innovation & Technology.
Brain for Robots
To break new ground in robotic perception, the NUS team explored neuromorphic technology — an area of computing that emulates the neural structure and operation of the human brain — to process sensory data from the artificial skin. As Tee and Soh are members of the Intel Neuromorphic Research Community, they decided to use Intel's Loihi research chip for their new robotic system.
In their initial experiments, the researchers fitted a robotic hand with the artificial skin, and used it to read Braille, passing the tactile data to Loihi via the cloud to convert the micro bumps felt by the hand into a semantic meaning. Loihi achieved over 92 percent accuracy in classifying the Braille letters, while using 20 times less power than a normal microprocessor.
Soh's team improved the robot's perception capabilities by combining both vision and touch data in a spiking neural network. In their experiments, the researchers tasked a robot equipped with both artificial skin and vision sensors to classify various opaque containers containing differing amounts of liquid. They also tested the system's ability to identify rotational slip, which is important for stable grasping.
In both tests, the spiking neural network that used both vision and touch data was able to classify objects and detect object slippage. The classification was 10 percent more accurate than a system that used only vision. Moreover, using a technique developed by Soh's team, the neural networks could classify the sensory data while it was being accumulated, unlike the conventional approach where data is classified after it has been fully gathered. In addition, the researchers demonstrated the efficiency of neuromorphic technology: Loihi processed the sensory data 21 percent faster than a top performing graphics processing unit, while using more than 45 times less power.
"We're excited by these results," Soh says. "They show that a neuromorphic system is a promising piece of the puzzle for combining multiple sensors to improve robot perception. It's a step towards building power-efficient and trustworthy robots that can respond quickly and appropriately in unexpected situations."
"This research from the National University of Singapore provides a compelling glimpse to the future of robotics where information is both sensed and processed in an event-driven manner combining multiple modalities," says Mike Davies, director of Intel's Neuromorphic Computing Lab. "The work adds to a growing body of results showing that neuromorphic computing can deliver significant gains in latency and power consumption once the entire system is re-engineered in an event-based paradigm spanning sensors, data formats, algorithms, and hardware architecture,"
This research was supported by the National Robotics R&D Programme Office, a set-up that nurtures the robotics ecosystem in Singapore through funding research and development to enhance the readiness of robotics technologies and solutions.
Moving forward, Tee and Soh plan to further develop their novel robotic system for applications in the logistics and food manufacturing industries where there is a high demand for robotic automation, especially moving forward in the post-COVID era.