A team of computer scientists and material engineers from National University of Singapore (NUS) yesterday unveiled an innovative research aimed at making robots smarter by developing a sensory integrated artificial brain system that mimics the human neural networks.
Combining artificial skin and vision sensors, the new system seeks to provide robots with a sense of touch and significantly increasing its ability to process sensory information quickly and intelligently.
“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. 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,” said Assistant Professor Benjamin Tee from NUS Materials Science and Engineering. He co-leads this project with Assistant Professor Harold Soh from NUS Computer Science.
The NUS research was supported by the National Robotics R&D Programme Office (NR2PO), a set-up that nurtures the robotics ecosystem in Singapore through funding research and development (R&D) to enhance the readiness of robotics technologies and solutions. Key considerations for NR2PO’s R&D investments include the potential for impactful applications in the public sector, and the potential to create differentiated capabilities for our industry.
The findings of this cross-disciplinary work were presented at the renowned conference Robotics: Science and Systems conference this month.
Getting the human touch
Most of today’s robots operate solely based on visual processing, which limits their capabilities. For instance, picking up a soft drink can is a complex task for robots - it has to locate the object, deduce its shape, determine the right amount of strength to use, and grasp the object without letting it slip.
In the new robotic system, the NUS team applied an advanced artificial skin known as Asynchronous Coded Electronic Skin (ACES) developed by Asst Prof Tee and his team in 2019. This novel 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.
Enabling a human-like sense of touch in robotics could significantly improve current functionality, and even lead to new uses. On the 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.
“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,” said Tee, who is also from the NUS Institute for Health Innovation & Technology.
A human-like 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 both Tee and Soh are members of the Intel's Neuromorphic Research Community (INRC), it was a natural choice to use Intel’s Loihi neuromorphic research chip for their new robotic system.
Commenting on the NUS research, Mike Davis, director of Intel’s Neuromorphic Computing Lab, said it provides a glimpse of the future “where information is both sensed and processed in an event-driven manner combining multiple modalities.”
“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,” Davis added.
In their initial experiments, the NUS 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% 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% more accurate than a system that used only vision. Moreover, using a technique developed by Asst Prof 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% faster than a top performing graphics processing unit (GPU), while using more than 45 times less power.
“We’re excited by these results. 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,” Soh said
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.
