In this episode, we take a closer look at the effect of novelty in human-robot interaction. Novelty is the quality of being new or unusual.
The typical view is that while something is new, or “a novelty”, it will initially make us behave differently than we would normally. But over time, as the novelty wears off, we will likely return to our regular behaviors. For example, a new robot may cause a person to behave differently initially, as its introduced into the person’s life, but after some time, the robot won’t be as exciting, novel and motivating, and the person might return to their previous behavioral patterns, interacting less with the robot.
To find out more about the concept of novelty in human-robot interactions, our interviewer Audrow caught up with Catharina Vesterager Smedegaard, a PhD-student at Aarhus University in Denmark, whose field of study is Philosophy.
Catharina sees novelty differently to how we typically see it. She thinks of it as projecting what we don’t know onto what we already know, which has implications for how human-robot interactions are designed and researched. She also speaks about her experience in philosophy more generally, and gives us advice on philosophical thinking.
Catharina Vesterager Smedegaard
Catharina Vesterager Smedegaard started as a PhD-student the 1st of December 2017. She has a BA and MA in philosophy. In autumn 2015, Catharina interned at the research group PENSOR (the present RUR), where she first became interested in Social Robotics and formed the idea for her MA thesis.
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By Aviral Kumar
One of the primary factors behind the success of machine learning approaches in open world settings, such as image recognition and natural language processing, has been the ability of high-capacity deep neural network function approximators to learn generalizable models from large amounts of data. Deep reinforcement learning methods, however, require active online data collection, where the model actively interacts with its environment. This makes such methods hard to scale to complex real-world problems, where active data collection means that large datasets of experience must be collected for every experiment – this can be expensive and, for systems such as autonomous vehicles or robots, potentially unsafe. In a number of domains of practical interest, such as autonomous driving, robotics, and games, there exist plentiful amounts of previously collected interaction data which, consists of informative behaviours that are a rich source of prior information. Deep RL algorithms that can utilize such prior datasets will not only scale to real-world problems, but will also lead to solutions that generalize substantially better. A data-driven paradigm for reinforcement learning will enable us to pre-train and deploy agents capable of sample-efficient learning in the real-world.
In this work, we ask the following question: Can deep RL algorithms effectively leverage prior collected offline data and learn without interaction with the environment? We refer to this problem statement as fully off-policy RL, previously also called batch RL in literature. A class of deep RL algorithms, known as off-policy RL algorithms can, in principle, learn from previously collected data. Recent off-policy RL algorithms such as Soft Actor-Critic (SAC), QT-Opt, and Rainbow, have demonstrated sample-efficient performance in a number of challenging domains such as robotic manipulation and atari games. However, all of these methods still require online data collection, and their ability to learn from fully off-policy data is limited in practice. In this work, we show why existing deep RL algorithms can fail in the fully off-policy setting. We then propose effective solutions to mitigate these issues.
At Danfoss in Gråsten, the Danish Technological Institute (DTI) is testing, as part of a pilot project in the European robot network ROBOTT-NET, several robot technologies: Manipulation using force sensors, simpler separation of items and a 3D-printed three-in-one gripper for handling capacitors, nuts and a socket handle.
“The set-updemonstrates various techniques that provide a cheaper solution, increased robustness and increased safety for operators”, says Søren Peter Johansen Technology Manager at DTI.
“For example, there is a force-torque sensor in the robot which is used to manoeuvre things carefully into place, and also to increase the confidence of the operators. The force-torque sensor will sense and prevent collisions with people before the built-in safety features of the robot stop the robot arm. Thus, we can also increase safety and confidence by working with collaborative robots”, he says.
Increased effectiveness
Danfoss in Gråsten has tested the robot in connection with the company’s production of frequency converters.
“A frequency converter contains, amongst other things, capacitors assembled in groups of two or four. We have automated this process in that a robot picks up a capacitor and puts it into a fixture. For the capacitor to be correctly placed in the fixture, the electrodes must point in the right direction. Therefore, a camera sees how the electrodes are oriented in the gripper, and orients correctly before it is placed in the fixture”, explains Søren Peter Johansen.
“Then we pick a nut that has to be put on and the robot picks up the socket handle and screws the nut tight. And meanwhile the operator can do something else and thus increases efficiency”, he elaborates.
Three reasons to automate
Peter Lund Andersen, Senior Manufacturing Specialist at Danfoss, says that Danfoss is automating for several reasons:
“It is primarily about work and environmental considerations – better ergonomics and less heavy lifting. We believe that with robots, that can do the work uniformly each time, we can increase the quality of our products while maintaining safety and reducing costs”, he says, adding “The cooperation with DTI gives us the opportunity to come out and try some new things. Some things that aren’t quite mature yet and can’t just be taken off a shelf. That way we are at the forefront with everything.”
If you want to watch more videos, you can explore ROBOTT-NET’s pilot projects on our YouTube-channel.