From Go to Dota 2 to the operation of commercial HVAC systems - reinforcement learning modeling and algorithms are changing how engineers see complicated dynamical systems as games, and learn strategies to play them well. Kindred is applying the revolutionary technology that powered AlphaGo to the creation of a new generation of intelligent robotics for material handling.
Abstract: Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is often unreliable and difficult, which resulted in their low adoption in reinforcement learning research. This difficulty is worsened by the lack of guidelines for setting up learning tasks with robots. In this work, we develop a learning task with a UR5 robotic arm to bring to light some key elements of a task setup and study their contributions to the challenges with robots. We find that learning performance can be highly sensitive to the setup, and thus oversights and omissions in setup details can make effective learning, reproducibility, and fair comparison hard. Our study suggests some mitigating steps to help future experimenters avoid difficulties and pitfalls. We show that highly reliable and repeatable experiments can be performed in our setup, indicating the possibility of reinforcement learning research extensively based on real-world robots.
Abstract: As the linguistic capabilities of interactive robots advance, it becomes increasingly important to understand how humans will instruct robots through natural language. What is more, with the increased use of teleoperated humanoid robots, it is important to recognize whether any differences between instructions given to humans and robots are due to the physical embodiment or perceived autonomy of the instructee. In this paper, we present the results of a human-subject experiment in which participants interacted in a collaborative, task-based setting with both a human and a suit-based, teleoperated humanoid robot said to be either autonomous or teleoperated. Our results suggest that humans will use politeness strategies equally with human, autonomous robotic, and teleoperated robotic teammates, reinforcing recent findings that autonomous robots must comprehend and appropriately respond to human utterances that follow such strategies. Our results also suggest variations in how different teammates were perceived. Specifically, our results suggest that human-teleoperated robots were perceived as less intelligent than human teammates; a finding with serious implications for human-robot team dynamics.