Within the HRI community, Wizard-of-Oz (WoZ) methods have been extensively used to explore robot interactions. However, their wide usage has also been criticized as not giving good representation of how real, autonomous robots can interact. In this paper, I argue that thoughtfully designed Wizard-of-Oz control interfaces can enable robot designers to explore the interaction design space fur human robot interaction and also move them towards autonomous robot development. I provide two case studies of WoZ controlled robots and their interfaces and describe the design rationale behind them. I then provide an initial set of design considerations for future WoZ controllers as a way to improve how WoZ interactions can inform the design of autonomous behavior and move closer to fully autonomous robots.

Certain stimuli in the environment reliably, and perhaps inevitably, trigger human cognitive and behavioral responses. We suggest that the presence of such “trigger stimuli” in modern robots can have disconcerting consequences. We provide one new example of such consequences: a reversal of a pattern of moral judgments people make about robots, depending on whether they view a “mechanical” or a “humanoid” robot.

Robot designers commonly emphasize humanlikeness as an important design feature to make robots social or user-friendly. To understand how users make sense of the design characteristics of robots, we asked 6 participants to classify and interpret the appearance of existing robots in relation to their function and potential usefulness. All the robots had humanlike aspects in their design, and participants most commonly remarked on these humanlike features of the robots. However, the commonsense logic of the “Uncanny Valley” (UV) in HRI design, which suggests that robots should be similar to humans to some degree without being too humanlike, was not supported by participant comments, which did not correlate humanlikeness to user-friendliness in line with the UV hypothesis. Rather, participants related the design features of robots to their everyday contexts, and focused their commentary on context-dependent design implications. As a result, we suggest our understanding of the design characteristics of robots should include the perspectives of users from the earliest stages of design so we can understand their contextual interpretations of different design characteristics. Open and modularized technical platforms could support the inclusion of users in the creation of future social robots.

We present the Robot Gesture Library (RoGuE), amotion-planning approach to generating gestures. Gestures improve robot communication skills, strengthening robots as partners in a collaborative setting. Previouswork maps from environment scenario to gesture selection. This work maps from gesture selection to gesture execution. We create a flexible and common language by parameterizing gestures as task-space constraints onrobot trajectories and goals. This allows us to leverage powerful motion planners and to generalize across environments and robot morphologies. We demonstrateRoGuE on four robots: HREB, ADA, CURI and the PR2.

Wizard-of-Oz (WoZ) is a common technique enabling HRI researchers to explore aspects of interaction not yet backed by autonomous systems. A standardized, open, and flexible WoZ framework could therefore serve the community and accelerate research both for the design of robotic systems and for their evaluation. This paper presents the definition of OpenWoZ , a Wizard-of-Oz framework for HRI, designed to be updated during operation by the researcher controlling the robot. OpenWoZ is implemented as a thin HTTP server running on the robot, and a cloud-backed multi-platform client schema. The WoZ server accepts representational state transfer (REST) requests from a number and variety of clients simultaneously. This "separation of concerns" in OpenWoZ allows addition of commands, new sequencing of behaviors, and adjustment of parameters, all during run-time.

Extended school absence during K-12 education can have anegative impact on both the educational and social development of a child. Mobile Remote Presence (MRP) can helpenable continued access to K-12 education for children withhealth challenges. However, most MRP platforms are targetedtowards adult users in domains such as the workplace.The importance of social interaction and engagement in K-12 education creates a unique set of needs and challenges foran MRP platform. In this work, we discuss the benefits ofMRP usage for K-12 education, ongoing challenges for MRPacross domains, and the requirements of an MRP platform forthe classroom.

We consider the problem of robot path planning in an environment where the location and geometry of obstacles are initially unknown while reusing relevant knowledge about collision avoidance learned from robots’ previous navigational experience. Our main hypothesis in this paper is that the path planning times for a robot can be reduced if it can refer to previous maneuvers it used to avoid collisions with obstacles during earlier missions, and adapt that information to avoid obstacles during its current navigation. To verify this hypothesis,we propose an algorithm called LearnerRRT that first uses a feature matching algorithm called Sample ConsensusInitial Alignment (SAC-IA) to efficiently match currently encountered obstacle features with past obstacle features, and, then uses an experience based learning technique to adapt previously recorded robot obstacle avoidance trajectories corresponding to the matched feature, to the current scenario. The feature matching and machine learning techniques are integrated into the robot’s path planner so that the robot can rapidly and seamlessly update its path to circumvent an obstacle it encounters, in real-time, and continue to move towards its goal. We have conducted several experiments using a simulated Coroware Corobot robot within the Webots simulator to verify the performance of our proposed algorithm,with different start and goal locations, and different obstacle geometries and placements, as well as compared our approach to a state-of-the-art sampling based path planner. Our results show that the proposed algorithm LearnerRRT performs much better than InformedRRT*. When given the same time, our algorithm finished its task successfully whereas Informed RRT* could only achieve 10-20 percent of the optimal distance.

Team decision problems are one of the fundamental problems in decentralized decision making. Because team decision problems are NP-hard, it is important to find methods for reducing their complexity. Wu and Lall gave a definition of sufficient statisticsfor team decision problems, and demonstrated that these statistics are sufficient for optimality, and possess other desirable properties such as being readily updated when additional information becomes available. More recently, Lemon and Lall defined weak sufficient statistics for team decision problems, and showed that these statistics are sufficient for optimality and necessary for simultaneous optimality with respect to all cost functions. This prior work studied the extent to which the complexity of team decision problems can be reduced while maintaining exact optimality. However,when faced with a computationally difficult problem, we are often willing to sacrifice exact optimality for significant reductions in complexity. In this paper we define approximate sufficient statistics, which are a generalization of weak team sufficient statistics. Then we prove that these statistics are quantifiably close to being optimal.

We consider the effects of a human in the loop with respect to dramatic behavior in a team environment in this position paper. This dramatic behavior is captured mathematically as a jump in behavior. We cite recent examples and discuss earlier work in the cognitive sciences. We consider the problem in light of network science.

It is nowadays our responsibility to convince our contemporary citizens that AI devices as UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles) are crucial actors of today’s life in a dual domains, both civilian and military. In particular, the decision process is the main component of every military operation and is of high interest because of two main reasons : it is necessary designed to cope with conflict issues and it requires a very complex planning process to be successful. The difficulty to find a good plan is worse in urban areas because of the high uncertainty due to the topology of these areas, the presence of civilians, who can be hostile or friendly, and the unpredictable nature of enemies. The idea in that paper is to qualify what can be a valid computed plan in that context , i.e. welldesigned for recovering of peace, rescue operations after a bombing event, hostage salvage, non-combatant evacuation operations, civil-military co-operation, ...., in urban areas. This planning process leads to associate actually four components, the representation of the tactical scheme, the implementation of the tactical scheme as the behaviour of special forces, military units or emergency squads, the proof process or the explanation process, and finally the handling of external factors depending on the current environment or the current context in which the operation takes place. This paper uses a quaternary representation called the epistemological quadriptych, in order to highlight that the integration of UGVs or UAVs devices requires actually to understand the role of knowledge and behaviour and to provide secure and valid action plans, i.e. which can be explained and justified.