Smarter robots, at your service
By: Qut Science
Tags: Robotics, Science, AI, Technology

Teaching robots to understand where they are and how they can interact with their environment is the key to unlocking the next generation of automated service systems.

Dr Niko Sünderhauf has been awarded a competitive and prestigious Amazon Research Award for 2019, receiving $120,000 in funding to investigate robotic models for navigation, manipulation and interaction.

Sünderhauf is a leader for the visual learning and understanding program at the QUT Centre for Robotics. His research focuses on how robots can perceive the world around them, understand the objects they see, and use that information to complete tasks.

Image: miriam-doerr/Getty

Sophisticated semantics

The new project, funded by the Amazon Research Award, will build on existing methods of robotic vision and navigation to make them more sophisticated and capable.

“Current robotic algorithms use camera images to create a map of their environment, but this only supports a very simplistic understanding of objects in the environment,” Sünderhauf said.

“Robots today can get a good understanding of where things are around them, but not what these objects are, and what the robot — or humans — could do with them. This simplified model of the world isn’t sophisticated enough to inform higher levels of learning, often making it impossible for a robot to learn complex tasks.”

Sünderhauf is investigating the possibilities of object-oriented semantic mapping to develop more meaningful maps of environments, which robots can navigate and interact within.

“Object-oriented semantic mapping means that robots have learned to detect and distinguish different objects, and can create a map of all the objects in an environment, each with its own location, type, and functionality,” Sünderhauf explained.

While the robot still uses the camera to see, its interpretation of the environment is based on a deeper understanding of objects.

“Rather than using camera data to recognise visually what a singular fridge in a particular environment looks like, the robot will create a deeper understanding of what a fridge is, how it acts, what’s kept inside it, and how to open the door — things like that. When it moves into new environments, it will be able apply that knowledge to unknown settings.”

Good robot!

Once the robot begins to formulate ideas of semantic meaning for objects within its environment, researchers have to devise a way to help the robot learn appropriate interactions with those objects.

Optimising a robot’s interactions is managed by rewarding correct behaviour, using a process called Deep Reinforcement Learning.

“It’s probably not that different from training a dog,” Sünderhauf said.

“We’re providing a reward signal when the robot exhibits correct behaviour and interactions in the environment. The robot is programmed to optimise its behaviour to seek as many reward signals as it can, so that reinforces correct actions.

“Investigating the limitations and strengths of Deep Reinforcement Learning in this context is a key focus of our project.”

Dr Niko Sünderhauf in the QUT Centre for Robotics. Image: SEF Communications

Mapping environments

Sünderhauf and his team are creating rich, descriptive maps of environments called graph-based maps to help the robots interpret their surroundings.

Unlike traditional maps with roads and landmarks, these maps are made up of nodes and links, coming together to describe an environment and its contents.

“Nodes in the graph represent objects, like a sink, a pen, or a knife. Other nodes represent locations in the environment the robot can navigate to. The links between nodes represent potential interactions, like how a robot could reach them from certain positions within the environment,” Sünderhauf explained.

These graph-based maps are the key to robots being able to interact more robustly in an environment.

“When the model of the environment is more detailed and more meaningful, the robot can learn better, act faster and function more efficiently and successfully within the environment as its understanding of that environment grows,” Sünderhauf said.

While the team are preparing the maps and programming them into the robotic models initially, robots would be able to use simultaneous localisation and mapping (SLAM) techniques to build these maps themselves in the future.

“SLAM is when a robot builds a map inside its ‘brain’ as it moves within an environment, while also localising itself with the help of the still incomplete map,” Sünderhauf explained.

“The robot can use the map to keep track of where objects are and how to move around them as it goes.”

Robo-butlers of the future

Sünderhauf’s project is focused on robots in domestic settings, completing tasks like helping clean the house, finding lost objects, or fetching a delivery from the front door.

Image: AndreyPopov/Getty

“Beyond that, it can have a huge impact in specialised care robots, like those working in hospitality, assisted living or aged care. It’ll be really useful for logistical robots, like delivery robots that have to interact with partially unknown environments to complete their task, including navigating gates and doors.”

It could also mean that robots who interact with humans in logistics or manufacturing will perform better, as they will be faster to train, better at understanding different appearances, and ultimately safer.

The key challenge for Sünderhauf and his team will be taking the robot from simulated test environments to unknown test environments. At the moment, they’re testing their robotic models in a simulation platform.

“It’s quite a rich simulation environment. The robot can do things like open a fridge, take a loaf of bread out, slice it, and put the bread in a toaster,” Sünderhauf said.

“Simulation is great for our research because it allows robots to do things that are currently hard for them to do in the real world, like articulation and grasping.”

Other research teams at QUT are working on projects dedicated to grasping arms and robotic manipulation, and Sünderhauf is excited for the opportunity to develop his project in parallel.

Where to from here?

Over the course of this one-year project, Sünderhauf and his team will build on the proof of concept that won them the Amazon Research Award.

“Our proof of concept demonstrated that the robot could find objects, like house keys or a television remote, in a simulated environment, using this graph-based reinforcement learning.

“Our initial investigations worked across both training and unseen environments, so we know that what we want to do is possible.”

Sünderhauf is enthusiastic about the opportunity to develop key fundamental research in robotics.

“We’re looking at creating a whole new generation of robotic learning. We’re very excited to see where this takes us,” he said.

More information

Explore more research at QUT’s Science and Engineering Faculty.

Find out more about the QUT Centre for Robotics.

Keep up to date with the project, Learning Robotic Navigation and Interaction from Object-based Semantic Maps.

Visit Dr Niko Sünderhauf’s website.