Robotic sound localization has traditionally been restricted to either on-robot microphone arrays or embedded microphones in aware environments, each of which have limitations due to their static configurations. This work overcomes the static configuration problems by using visual localization to track multiple wireless microphones in the environment with enough accuracy to combine their auditory streams in a traditional localization algorithm. In this manner, microphones can move or be moved about the environment, and still be combined with existing on-robot microphones to extend array baselines and effective listening ranges without having to re-measure inter-microphone distances.

A 3D optical flow tracking system was used to track participants as they watched a series of boring videos. The video stream of the participants was rated for boredom events. Ratings and head position data were combined to predict boredom events.

Tracking people and objects is an enabling technology for many robotic applications. From human-robot-interaction to SLAM, robots must know what a scene contains and how it has changed, and is changing, before they can interact with their environment. In this paper, we focus on the tracking necessary to record the 3D position and pose of objects as they change in real time. We develop a tracking system that is capable of recovering object locations and angles at speeds in excess of 60 frames per second, making it possible to track people and objects undergoing rapid motion and acceleration. Results are demonstrated experimentally using real objects and people and compared against ground truth data.

We describe a computational cognitive architecture for robots which we call ACT-R/E (ACT-R/Embodied). ACT-R/E is based on ACT-R [1, 2] but uses different visual, auditory, and movement modules. We describe a model that uses ACT-R/E to integrate visual and auditory information to perform conversation tracking in a dynamic environment. We also performed an empirical evaluation study which shows that people see our conversational tracking system as extremely natural.

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.

This paper addresses robust recovery of structure and motion for rigid bodies in video sequences. For small inter-frame motion, feature appearance is commonly estimated according to the previous frame. However, in the case of occlusion or corrupt frames, the small interframe motion model fails. In this paper, we propose to use robust identification techniques to estimate the motion dynamics based on a set of previous frames. These dynamics are then recursively used to robustly estimate object structure and motion and to predict the object appearance in future frames. Results are tested for 3D reconstructions of rigid bodies in real and synthetic image sequences.