Nick DePalma

Crowdsourcing human-robot interaction: new methods and system evaluation in a public environment

Supporting a wide variety of interaction styles across a diverse set of people is a significant challenge in human-robot interaction (HRI). In this work, we explore a data-driven approach that relies on crowdsourcing as a rich source of interactions that cover a wide repertoire of human behavior. We first develop an online game that requires two players to collaborate to solve a task. One player takes the role of a robot avatar and the other a human avatar, each with a different set of capabilities that must be coordinated to overcome challenges and complete the task. Leveraging the interaction data recorded in the online game, we present a novel technique for data-driven behavior generation using case-based planning for a real robot. We compare the resulting autonomous robot behavior against a Wizard of Oz base case condition in a real-world reproduction of the online game that was conducted at the Boston Museum of Science. Results of a post-study survey of participants indicate that the autonomous robot behavior matched the performance of the human-operated robot in several important measures. We examined video recordings of the real-world game to draw additional insights as to how the novice participants attempted to interact with the robot in a loosely structured collaborative task. We discovered that many of the collaborative interactions were generated in the moment and were driven by interpersonal dynamics, not necessarily by the task design. We explored using bids analysis as a meaningful construct to tap into affective qualities of HRI. An important lesson from this work is that in loosely structured collaborative tasks, robots need to be skillful in handling these in-the-moment interpersonal dynamics, as these dynamics have an important impact on the affective quality of the interaction for people. How such interactions dovetail with more task-oriented policies is an important area for future work, as we anticipate such interactions becoming commonplace in situations where personal robots perform loosely structured tasks in interaction with people in human living spaces.

Crowdsourcing human-robot interaction: Application from virtual to physical worlds

The ability for robots to engage in interactive behavior with a broad range of people is critical for future development of social robotic applications. In this paper, we propose the use of online games as a means of generating large-scale data corpora for human-robot interaction research in order to create robust and diverse interaction models. We describe a data collection approach based on a multiplayer game that was used to collect movement, action and dialog data from hundreds of online users. We then study how these records of human-human interaction collected in a virtual world can be used to generate contextually correct social and task-oriented behaviors for a robot collaborating with a human in a similar real-world environment. We evaluate the resulting behavior model using a physical robot in the Boston Museum of Science, and show that the robot successfully performs the collaborative task and that its behavior is strongly influenced by patterns in the crowdsourced dataset.

Exploiting social partners in robot learning

Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can influence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot ‘playmates’.

Crowdsourcing Real World Human-Robot Dialog and Teamwork through Online Multiplayer Games

100 AI MAGAZINE We envision the need for robots to be not only functional, but adaptable, robust to the diversity of human behaviors and speech patterns, and capable of acting in a both task and socially appropriate manner. Natural and diverse human-robot interaction (HRI) of this kind has been a long-standing goal for robotics research, and a broad range of approaches have been proposed for the development of robots that support diverse interactions. Among proposed techniques, variants that are dependent on hand-coded rule sets and probabilistic single-task policy learning methods have proven to be too brittle for interactive applications, failing to generalize over the diversity of possible inputs. Such systems typically force the user to adapt their method of interaction to fit the coded requirements of the robot. A different approach to creating more humanlike robotic systems has focused on imitating human cognitive processes by developing large scale cognitive architectures that support many modalities and interaction styles. While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. Common

Computational benefits of social learning mechanisms: Stimulus enhancement and emulation

Social learning in robotics has largely focused on imitation learning. In this work, we take a broader view of social learning and are interested in the multifaceted ways that a social partner can influence the learning process. We implement stimulus enhancement and emulation on a robot, and illustrate the computational benefits of social learning over individual learning. Additionally we characterize the differences between these two social learning strategies, showing that the preferred strategy is dependent on the current behavior of the social partner. We demonstrate these learning results both in simulation and with physical robot ‘playmates’.

Effects of social exploration mechanisms on robot learning

Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can influence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot ‘playmates’.

Computational origami: a creative blend of mathematics and art

To some, mathematics is an art form. In this interview, we discuss the creativity behind computational origami, a growing area of computational geometry, with Erik Demaine.

Interactive robot task learning

In this paper we provide a brief overview of our research agenda in Human-Robot Interaction and Interactive Learning. We highlight key components to be demonstrated as part of the CHI 2010 Media Showcase.