A robot coexisting with humans must not only be able to successfully perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many social settings, this involves the use of social signals like gaze, facial expression, and language. In this paper we discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We discuss how social states are inferred from low-level sensors, using vision and speech as input modalities, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study with human subjects, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

For the long term co-existence of robots with us in complete harmony, they will be expected to show sociocognitive behaviors. In this paper, taking inspiration from child development research and human behavioral psychology we will identify the basic but key capabilities: perceiving abilities, effort and affordances. Further we will present the concepts, which fuse these components to perform multi-effort ability and affordance analysis. We will show instantiations of these capabilities on real robot and will discuss its potential applications for more complex socio-cognitive behavior.

The most critical moves that determine the success of a manipulation task are performed within the close vicinities of the object prior to grasping, and the goal prior to the final placement. Memorizing these state-action sequences and reusing them can dramatically improve the task efficiency, whereas even the state-of-the-art planning algorithms may require significant amount of time and computational resources to generate a solution from scratch depending on the complexity and the constraints of the task. In this paper, we propose a hybrid approach that combines the reliability of the past experiences gained through demonstration and the flexibility of a generative motion planning algorithm, namely RRT*, to improve task execution efficiency. As a side benefit of reusing these final moves, we can dramatically reduce the number of nodes used by the generative planner, hence the planning time, by either early-terminating the planner when the generated plan reaches a "recalled state", or deliberately biasing it towards the memorized state-action sequences that are feasible at the moment. This complementary combination of the already available partial plans and the generated ones yield to fast, reliable, and repeatable solutions.

Existing robot guides are able to offer a tour of a building, such as a museum, bank, science center, to a single person or to a group of participants. Usually the tours are predefined and there is no support for dynamic interactions between multiple robots. This paper focuses on distributed collaboration between several robot guides providing a building tour to groups of participants. Semantic techniques are adopted in order to formally define the tour topics, available content on a specific topic, and the robot and human profiles including their interests and content knowledge. The robot guides select different topics depending on their participants' interests and prior knowledge. Optimization of the topics of interests is achieved through exchange of participants between the robot guides whenever in each others neighborhood. Evaluation of the implemented algorithms presents a 90% content coverage of relevant topics for the individual participants.

Many tasks for autonomous agents or robots are best de- scribed by a specification of the environment and a specifi- cation of the available actions the agent or robot can perform. Combining such a specification with the possibility to imper- atively program a robot or agent is what we call the action- based imperative programming. One of the most successful such approaches is Golog. In this paper, we draft a proposal for a new robot program- ming language YAGI, which is based on the action-based imperative programming paradigm. Our goal is to design a small, portable stand-alone YAGI interpreter. We combine the benefits of a principled domain specification with a clean, small and simple programming language, which does not ex- ploit any side-effects from the implementation language. We discuss general requirements of action-based programming languages and outline YAGI, our action-based language ap- proach which particularly aims at embeddability.

Reaching for a target requires estimating the spatial position of the target and to convert such a position in a suitable arm-motor command. In the proposed framework, the location of the target is represented implicitly by the gaze direction of the robot and by the distance of the target. The NAO robot is provided with two cameras, one to look ahead and one to look down, which constitute two independent head-centered coordinate systems. These head-centered frames of reference are converted into reaching commands by two neural networks. The weights of networks are learned by moving the arm while gazing the hand, using an on-line learning algorithm that maintains the covariance matrix of weights. This work adapts a previously proposed model that worked on a full humanoid robot torso, to work with the NAO and is a step toward a more generic framework for the implicit representation of the peripersonal space in humanoid robots.

Organizing committee for the  Cognitive Robotics Workshop held at AAAI-12.

The problem of designing and building effective assistive systems for human agents performing professional knowledge-intensive activities, or knowledge-works is of great interest and has wide implications. In this paper we propose a new approach for solving the problem. The approach is based on activity-context aware computation paradigm that can lead to flexible yet robust systems for holistic support in performing complex knowledge-works. To this end, we also outline here the notion of activity-context and the idea of activity-models as core artifacts used by such systems embodying the notion.

Knowledge workers work on many different tasks and must often switch between those tasks. In earlier work, we have shown the benefits of automatically capturing contexts for tasks for a specific category of knowledge worker, software programmers. Captured contexts facilitate task switches and reduce information overload by enabling the display of only the information relevant to the task-at-hand. In this paper, we describe the results of two studies of the use of captured contexts for a broad range of knowledge workers. The first study we describe is a field study of eight knowledge workers who used the model in their daily work for up to 25 days on tasks involving both file and web documents. We found that these knowledge workers need information to decay from their context and that our model is adequate at automatically trimming contexts. The second study is a case study of the use of contexts to support the operations of a software development company. We analyzed task contexts from hundreds of days of work from three users and found similar trends of information decaying from contexts. Results from each study also shed more light on the nature of mixed artifact task contexts.

Rapid development in the area of ambient intelligence introduced numerous applications. One of the fundamental underpinnings in such applications is an effective and reliable context-aware system able to recognize and understand activities performed by a human, and context in which it happened. However, there are two pending issues: (i) transferability, i.e., a specific implementation is tightly interrelated with a selected algorithm, available sensors, and a scenario/environment where they are employed; and (ii) comparability, i.e., there is no established benchmark problem that would enable a direct comparison of the developed context-aware systems. This paper first reviews some recent initiatives that address the abovementioned problems and then proposes a centralized collection of resources related to design and evaluation of context-aware systems. The main idea is to establish an online repository of datasets accompanied with the task, result and applied approach. Ideally, the contributors will provide the dataset with short description of the data, task and results, relevant paper, and link to resources such as implementation of the approach, preprocessing tools, and filtering. This would allow the community to quickly start building upon the latest state-of-the-art approaches, to benchmark newly developed techniques, and ultimately, to advance the frontiers in ambient intelligence.