We present a novel method for the visual detection of a contingent response by a human to the stimulus of a robot action. Contingency is defined as a change in an agent's behavior within a specific time window in direct response to a signal from another agent; detection of such responses is essential to assess the willingness and interest of a human in interacting with the robot. Using motion-based features to describe the possible contingent action, our approach assesses the visual self-similarity of video subsequences captured before the robot exhibits its signaling behavior and statistically models the typical graph-partitioning cost of separating an arbitrary subsequence of frames from the others. After the behavioral signal, the video is similarly analyzed and the cost of separating the after-signal frames from the before-signal sequences is computed; a lower than typical cost indicates likely contingent reaction. We present a preliminary study in which data were captured and analyzed for algorithmic performance.

Turn-taking is fundamental to the way humans engage in information exchange, but robots currently lack the turn-taking skills required for natural communication. In order to bring effective turn-taking to robots, we must first understand the underlying processes in the context of what is possible to implement. We describe a data collection experiment with an interaction format inspired by “Simon says,” a turn-taking imitation game that engages the channels of gaze, speech, and motion. We analyze data from 23 human subjects interacting with a humanoid social robot and propose the principle of minimum necessary information (MNI) as a factor in determining the timing of the human response.We also describe the other observed phenomena of channel exclusion, efficiency, and adaptation. We discuss the implications of these principles and propose some ways to incorporate our findings into a computational model of turn-taking.

One of the common applications for outdoor robots is to follow a path in large scale unknown environments. This task is challenging due to the intensive memory requirements to represent the map, uncertainties in the location estimate of the robot and unknown terrain type and obstacles on the way to the goal. We develop a novel graph-based path planner that is based on only local perceptual information to plan a path in such environments. In order to extend the capabilities of the graph representation, we introduce Exploration Bias, which is a node attribute that can implicitly encode obstacle features at immediate surrounding of a node in the graph, the uncertainty of the planner about a node location and also the frequency of visiting a location. Through simulation experiments, we demonstrate that the resulting path cost and distance that the robot traverses to reach the goal location is not significantly different from those of the previous approaches.

In a Robot Learning from Demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose a biologically-inspired computational model that enables the robot to focus on the task-relevant objects. To filter out incompatible task models, we compute a Task Relevance Value (TRV) for each object, which shows a human demonstrator's implicit indication of the relevance to the task. By combining an intentional action representation with ‘motionese’ [2], our model exhibits recognition capabilities compatible with the way that humans demonstrate. We evaluate the system on demonstrations from five different human subjects, showing its ability to correctly focus on the appropriate objects in these demonstrations.

The Rapidly Exploring Random Tree (RRT) algorithm can be applied to the robotic path planning problem and performs well in challenging, dynamic domains. Traditional RRT methods use a binary cost function and they select portions of the tree for expansion based on the Euclidean distance to the target. However, in outdoor navigation, the relative cost of terrain can also provide useful input to a planning algorithm that traditional RRT methods cannot take advantage of. We present the Metric Adaptive RRT (MA-RRT), which integrates planning and fast execution for generating paths over a cost map. The MA-RRT algorithm considers underlying cost of a path when calculating the distance function for tree expansion. A heuristic value is also used for determining distance from a point to the target and an adaptive mechanism is employed for adjusting the heuristic on-line. We have implemented our approach in offline simulations and in outdoor robot experiments, and show that the MA-RRT algorithm can improve upon the quality of the path returned when cost is considered. The trade off between cost consideration and runtime performance is also presented.