Creating a human-robot interface is a daunting experience. Capabilities and functionalities of the interface are dependent on the robustness of many different sensor and input modalities. For example, object recognition poses problems for state-of-the-art vision systems. Speech recognition in noisy environments remains problematic for acoustic systems. Natural language understanding and dialog are often limited to specific domains and baffled by ambiguous or novel utterances. Plans based on domain-specific tasks limit the applicability of dialog managers. The types of sensors used limit spatial knowledge and understanding, and constrain cognitive issues, such as perspective-taking.In this research, we are integrating several modalities, such as vision, audition, and natural language understanding to leverage the existing strengths of each modality and overcome individual weaknesses. We are using visual, acoustic, and linguistic inputs in various combinations to solve such problems as the disambiguation of referents (objects in the environment), localization of human speakers, and determination of the source of utterances and appropriateness of responses when humans and robots interact. For this research, we limit our consideration to the interaction of two humans and one robot in a retrieval scenario. This paper will describe the system and integration of the various modules prior to future testing.

How do children learn how to play hide and seek? At age 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. We show through a case study that a 3 1/2 year old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. We propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. We model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. We also take the "hiding" model and use it as the basis for a "seeking" model. We suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

In this paper, we describe a prototype interface that facilitates the control of a mobile robot team by a single operator, using a sketch interface on a tablet PC. The user sketches a qualitative map of the scene and includes the robots in approximate starting positions. Both path and target position commands are supported as well as editing capabilities. Sensor feedback from the robots is included in the display such that the sketch interface acts as a two-way communication device between the user and the robots. The paper also includes results of a usability study, in which users were asked to perform a series of tasks

In conversation, people often use spatial relationships to describe their environment, e.g., "There is a desk in front of me and a doorway behind it," and to issue directives, e.g., "go around the desk and through the doorway." In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multimodal robot interface. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog. Examples using spatial language are included for both robot-to-human feedback and also human-to-robot commands. We also discuss some linguistic consequences in the semantic representations of spatial and locative information based on this work.

In our research on intuitive means for humans and intelligent, mobile robots to collaborate, we use a multimodal interface that supports speech and gestural inputs. As a preliminary step to evaluate our approach and to identify practical areas for future work, we conducted a wizard-of-Oz pilot study with five participants who each collaborated with a robot on a search task in a separate room. The goal was to find a sign in the robot's environment with the word "FOO" printed on it. Using a subset of our multimodal interface, participants were told to direct the collaboration. As their subordinate, the robot would understand their utterances and gestures, and recognize objects and structures in the search space. Participants conversed with the robot through a wireless microphone and headphone and, for gestural input, used a touch screen displaying alternative views of the robot's environment to indicate locations and objects.

In conversation, people often use spatial relationships to describe their environment, e.g., "There is a desk in front of me and a doorway behind it", and to issue directives, e.g., "Go around the desk and through the doorway." In our research, we have been investigating the use of spatial relationships to establish a natural communication mechanism between people and robots, in particular, for novice users. In this paper, the work on robot spatial relationships is combined with a multimodal robot interface developed at the Naval Research Lab. We show how linguistic spatial descriptions and other spatial information can be extracted from an evidence grid map and how this information can be used in a natural, human-robot dialog.