Abstract-Nonverbal communication plays an important role in coordinating teammates' actions for collaborative activities. In this paper, we explore the impact of non-verbal social cues and behavior on task performance by a human-robot team. We report our results from an experiment where naïve human subjects guide a robot to perform a physical task using speech and gesture. The robot communicates either implicitly through behavior or explicitly through non-verbal social cues. Both selfreport via questionnaire and behavioral analysis of video offer evidence to support our hypothesis that implicit non-verbal communication positively impacts human-robot task performance with respect to understandability of the robot, efficiency of task performance, and robustness to errors that arise from miscommunication. Whereas it is already well accepted that social cues enhance the likeability of robots and animated agents, our results offer promising evidence that they can also serve a pragmatic role in improving the effectiveness human-robot teamwork where the robot serves as a cooperative partner.
This paper makes the case for designing interactive robots with their expressive movement in mind. As people are highly sensitive to physical movement and spatiotemporal affordances, well-designed robot motion can communicate, engage, and offer dynamic possibilities beyond the machines' surface appearance or pragmatic motion paths. We present techniques for movement centric design, including character animation sketches, video prototyping, interactive movement explorations, Wizard of Oz studies, and skeletal prototypes. To illustrate our design approach, we discuss four case studies: a social head for a robotic musician, a robotic speaker dock listening companion, a desktop telepresence robot, and a service robot performing assistive and communicative tasks. We then relate our approach to the design of non-anthropomorphic robots and robotic objects, a design strategy that could facilitate the feasibility of realworld human-robot interaction.
Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include-in the long term-robots for homes, hospitals, and offices, but already exist in more advanced settings, such as space exploration. The work reported in this paper is part of an ongoing collaboration with NASA JSC to develop Robonaut, a humanoid robot envisioned to work with human astronauts on maintenance operations for space missions. To date, work with Robonaut has mainly investigated performing a joint task with a human in which the robot is being teleoperated. However, perceptive disorientation, sensory noise, and control delays make teleoperation cognitively exhausting even for a highly skilled operator. Control delays in long range teleoperation also make shoulder-to-shoulder teamwork difficult. These issues motivate our work to make robots collaborating with people more autonomous. Our work focuses on a scenario of a human and an autonomous humanoid robot working together shoulder-to-shoulder, sharing the workspace and the objects required to complete a task. A robotic member of such a team must be able to work towards a shared goal, and be in agreement with the human as to the sequence of actions that will be required to reach that goal, as well as dynamically adjust its plan according to the human's actions. Human-robot collaboration of this nature is an important yet relatively unexplored kind of human-robot interaction. This paper describes our work towards building a dynamic collaborative framework enabling such an interaction. We discuss our architecture and its implementation for controlling a humanoid robot, working on a task with a human partner. Our approach stems from Joint Intention Theory, which shows that for joint action to emerge, teammates must communicate to maintain a set of shared beliefs and to coordinate their actions towards the shared plan. In addition, they must demonstrate commitment to doing their own part, to the others doing theirs, to providing mutual support, and finally-to a mutual belief as to the state of the task. We argue that to this end, the concept of task and action goals is central. We therefore present a goal-driven hierarchical task representation, and a resulting collaborative turn-taking system, implementing many of the above-mentioned requirements of a robotic teammate. Additionally, we show the implementation of relevant social skills supporting our collaborative framework. Finally, we present a demonstration of our system for collaborative execution of a hierarchical object manipulation task by a robot-human team. Our humanoid robot is able to divide the task between the participants while taking into consideration the collaborator's actions when deciding what to do next. It is capable of asking for mutual support in the cases where it is unable to perform a certain action. To facilitate this interaction, the robot actively maintains a clear and intuitive channel of communication to synchronize goals, task states, and actions, resultin...
In Socially Guided Machine Learning we explore the ways in which machine learning can more fully take advantage of natural human interaction. In this work we are studying the role real-time human interaction plays in training assistive robots to perform new tasks. We describe an experimental platform, Sophie's World, and present descriptive analysis of human teaching behavior found in a user study. We report three important observations of how people administer reward and punishment to teach a simulated robot a new task through Reinforcement Learning. People adjust their behavior as they develop a model of the learner, they use the reward channel for guidance as well as feedback, and they may also use it as a motivational channel.
This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robot's ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in people's daily lives.
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