In this position paper, we describe the RoboHelper project, its findings and our vision for its future. The long-term goal of RoboHelper is to develop assistive robots for the elderly. The main thesis of our work is that such robots must crucially be able to participate in multimodal dialogues. Contributions of our work to date include the ELDERLY-AT-HOME corpus that we collected and annotated. It consists of 20 task-oriented human-human dialogues between a helper and an elderly person in a fully functional apartment. The unique feature of the corpus is that in addition to video and audio, it includes recordings of physical interaction. Based on this data, we have demonstrated the crucial role that Haptic-Ostensive (H-O) actions play in interpreting language and uncovering a person's intentions. H-O actions manipulate objects, but they also often perform a referring function. Our models were derived on the basis of manually annotated categories. Additional experiments show that we can identify H-O actions using the physical interaction data measured through an unobtrusive sensory glove developed as part of the project. In future work, we will derive models for the robot to decide what to do next (as opposed to interpreting what the interlocutor did); explore other types of physical interactions; and refine preliminary implementations of our models on the Nao robotic platform.

Assessing the quality of robot social navigation is a challenging problem fraught with human obstacles. From preconceived notions to perspective or point of view, evaluations can differ from person to person. Most work in the field of robot navigation is focused on creating algorithms that produce efficient robot trajectories. We posit that the evaluation of trajectories in a social context is essential and distinct to trajectory generation. In this work we recorded a manually driven powered wheelchair through different scenarios and asked expert evaluators to assess the quality of the powered wheelchair's movement. These evaluations were then compared to post-experiment assessments from trajectory generation algorithms and social navigation concepts. Our results show that it is possible to build a simple model to predict expert evaluators' responses. Unfortunately, there is no clear consensus amongst these experts on what quality behaviour is. This suggests that while current navigation algorithms offer strong heuristics for the generation of smooth trajectories in well-defined environments, their efficacy in evaluating social navigation is less obvious. We believe that more emphasis must be put on dynamic and reactive navigation algorithms as any heuristic approach will be limited due to variance in people's behaviours and expectations.

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Some of its key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision-theoretic decision making, including one-shot decision making (e.g., Bayesian games) and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single-and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.

Nonverbal communication is a critical way for humans to relay information and can have many forms including hand gestures, touch, and facial expressions. Our work focuses on touch gestures. In typical systems the recognition process does not begin until after the communication has completed, which can create a delayed response from the robot. It may take time for the robot to plan the appropriate response to touch, which could delay the reaction time. We have trained an artificial neural network on features extracted from the Leap Motion Controller, and successfully performed early recognition of touch gestures with high accuracy.

The story is particular medium. However, the discourse layer is not simply a conceptualization of the world of the narrative, with the an ordered subset of elements of the story layer. Genette characters, actions and events that it contains, while the discourse argues that every discourse implies a narrator. In this, the is composed of the communicative elements that participate discourse is an intentional structure through which the narrator in its telling. Research on computational models of "regulates the narrative information" given to the audience, narrative has produced many models of story, based for instance and its representation should include these intentions.

Advanced robotic technology is opening up the possibility of integrating robots into the human workspace to improve productivity and decrease the strain of repetitive, arduous physical tasks currently performed by human workers. However, coordinating these teams is a challenging problem. We must understand how decision-making authority over scheduling decisions should be shared between team members and how the preferences of the team members should be included. We report the results of a human-subject experiment investigating how a robotic teammate should best incorporate the preferences of human teammates into the team's schedule. We find that humans would rather work with a robotic teammate that accounts for their preferences, but this desire might be mitigated if their preferences come at the expense of team efficiency.

In typical human interactions, nonverbal behaviors such as eye gazes and gestures serve to augment and reinforce spoken communication. To use similar nonverbal behaviors in human-robot interactions, researchers can apply artificial intelligence techniques such as machine learning, cognitive modeling, and computer vision. But knowledge of nonverbal behavior can also benefit artificial intelligence: because nonverbal communication can reveal human mental states, these behaviors provide additional input to artificial intelligence problems such as learning from demonstration, natural language processing, and motion planning. This article describes how nonverbal communication in HRI can benefit from AI techniques as well as how AI problems can use nonverbal communication in their solutions.

Emotions are a key element in all human interactions. It is well documented that individual- and group-level interactions have different emotional expressions and humans are by nature extremely competent in perceiving, adapting and reacting to them. However, when developing social robots, emotions are not so easy to cope with. In this paper we introduce the concept of emotional climate applied to human-robot interaction (HRI) to define a group-level emotional expression at a given time. By doing so, we move one step further in developing a new tool that deals with group emotions within HRI.

We parallelize the Point-Based Value Iteration (PBVI) algorithm, which approximates the solution to Partially Observable Markov Decision Processes (POMDPs), using a Graphics Processing Unit (GPU). We detail additional optimizations, such as leveraging the bounded size of non-zero values over all belief point vectors, usable by serial and parallel algorithms. We compare serial (CPU) and parallel (GPU) implementations on 10 distinct problem domains, and demonstrate that our approach provides an order of magnitude improvement.

We present an approach geared toward estimating task execution confidence for robotic bin-picking applications. This requires estimating execution confidence for all constituent subtasks including part recognition and pose estimation, singulation, transport, and fine positioning. This paper is focussed on computing associated confidence parameters for the part recognition and pose estimation subtask. In particular, our approach allows a robot to evaluate how good the part recognition and pose estimation is, based on a confidence-measure, and thereby determine whether to proceed with the task execution (part singulation) or to request help from a human in order to resolve the associated failure. The value of a mean-square distance metric at a local minimum where the part matching solution is found is used as a surrogate for the confidence parameter. Experiments with a Baxter robot are used illustrate our approach.