MIT scientists have developed a robot with "socially aware navigation," that can pace along side pedestrians, follow the basic traffic rules, and smoothly navigate in crowds without bumping into anyone.
Just as drivers observe the rules of the road, most pedestrians follow certain social codes when navigating a hallway or a crowded thoroughfare. These including keeping to the right, overtake from the left, maintain a respectable berth, and be ready to change course to avoid oncoming obstacles while keeping up a steady walking pace.
The robot, which resembles a knee-high kiosk on wheels, successfully avoided collisions while keeping up with the average flow of pedestrians.
"Socially aware navigation is a central capability for mobile robots operating in environments that require frequent interactions with pedestrians," said Yu Fan Chen, a former graduate student at Massachusetts Institute of Technology (MIT) in the US.
"For instance, small robots could operate on sidewalks for package and food delivery. Similarly, personal mobility devices could transport people in large, crowded spaces, such as shopping malls, airports, and hospitals," said Chen.
For a robot to make its way autonomously through a heavily trafficked environment, it must solve four main challenges: localisation (knowing where it is in the world), perception (recognising its surroundings), motion planning (identifying the optimal path to a given destination), and control (physically executing its desired path).
Researchers used standard approaches to solve the problems of localisation and perception.
For the latter, they outfitted the robot with off-the-shelf sensors, such as webcams, a depth sensor, and a high-resolution lidar sensor. For the problem of localisation, they used open-source algorithms to map the robot's environment and determine its position. To control the robot, they employed standard methods used to drive autonomous ground vehicles.
The tricky part was to navigate in pedestrian-heavy environments, where individual paths are often difficult to predict. Usually roboticists try to program a robot to compute an optimal path that accounts for everyone's movements.
"But this takes forever to compute. Your robot is just going to be parked, figuring out what to do next, and meanwhile the person's already moved way past it before it decides 'I should probably go to the right,'" Everett said.
The team found a way around such limitations, enabling the robot to adapt to unpredictable pedestrian behaviour while continuously moving with the flow and following typical social codes of pedestrian conduct.
They used reinforcement learning, a type of machine learning approach, in which they performed computer simulations to train a robot to take certain paths, given the speed and trajectory of other objects in the environment.
The team also incorporated social norms into this offline training phase, in which they encouraged the robot in simulations to pass on the right, and penalised the robot when it passed on the left.
