We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.
The work by @cite_3 uses sensor data from real world human-human interaction to train a robot for HRI. In our research we hope to initially use both simulation and real world sensor data from interaction and combine this data and then expand to data gained from interaction between our algorithm and a human in both simulated and real world environments. Rather than using human description of fixed actions and scenarios @cite_14 @cite_13 our initial data contains interactions between two humans. However, unlike @cite_3 we are using a human-as-a-robot approach which limits the abilities of the person in the "robot" role, to make their role analogous to the physical robot of our eventual system.
@cite_14 @cite_13 @cite_3
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Recent studies in human–robot interaction (HRI) have investigated ways to harness the power of the crowd for the purpose of creating robot interaction logic through games and teleoperation interfaces. Sensor networks capable of observing human–human interactions in the real world provide a potentially valuable and scalable source of interaction data that can be used for designing robot behavior. To that end, we present here a fully automated method for reproducing observed real-world social interactions with a robot. The proposed method includes techniques for characterizing the speech and locomotion observed in training interactions, using clustering to identify typical behavior elements and identifying spatial formations using established HRI proxemics models. Behavior logic is learned based on discretized actions captured from the sensor data stream, using a naive Bayesian classifier. Finally, we propose techniques for reproducing robot speech and locomotion behaviors in a robust way, despite the natural variation of human behaviors and the large amount of sensor noise present in speech recognition. We show our technique in use, training a robot to play the role of a shop clerk in a simple camera shop scenario, and we demonstrate through a comparison experiment that our techniques successfully enabled the generation of socially appropriate speech and locomotion behavior. Notably, the performance of our technique in terms of correct behavior selection was found to be higher than the success rate of speech recognition, indicating its robustness to sensor noise. This paper addresses some of the problems that arise when applying active learning to the context of human-robot interaction (HRI). Active learning is an attractive strategy for robot learners because it has the potential to improve the accuracy and the speed of learning, but it can cause issues from an interaction perspective. Here we present three interaction modes that enable a robot to use active learning queries. The three modes differ in when they make queries: the first makes a query every turn, the second makes a query only under certain conditions, and the third makes a query only when explicitly requested by the teacher. We conduct an experiment in which 24 human subjects teach concepts to our upper-torso humanoid robot, Simon, in each interaction mode, and we compare these modes against a baseline mode using only passive supervised learning. We report results from both a learning and an interaction perspective. The data show that the three modes using active learning are preferable to the mode using passive supervised learning both in terms of performance and human subject preference, but each mode has advantages and disadvantages. Based on our results, we lay out several guidelines that can inform the design of future robotic systems that use active learning in an HRI setting. In a typical Human-Robot Interaction (HRI) scenario, the robot needs to perform various tasks for the human, hence should take into account human oriented constraints. In this context it is not sufficient that the robot selects grasp and placement of the object from the stability point of view only. Motivated from human behavioral psychology, in this paper we emphasize on the mutually depended nature of grasp and placement selections, which is further constrained by the task, the environment and the human's perspective. We will explore essential human oriented constraints on grasp and placement selections and present a framework to incorporate them in synthesizing key configurations of planning basic interactive manipulation tasks. Human-robot interaction (HRI) is a key element for diffusion of robotised production. Clear advantages in flexibility and productivity are possible, when the two operators are free to interact, as they are endowed with complementary skills. To achieve such a goal, safety systems capable of coping with task and robot constraints have to be designed. In this paper, a collision avoidance strategy, tackling consistency with task constraints and robot kinematic limitations, is proposed. Robot joint velocities are selected with a QP optimisation problem, minimising the difference from evasive velocities, while respecting task constraints. Integration with an industrial controller is discussed as well, while the strategy is experimentally validated on a dual arm industrial robot prototype, working in close interaction with a human.