Inspired by somatic methodologies and neurophysiology, Haplos is a low-cost, wearable technology that applies vibrotactile patterns to the skin, can be incorporated in existing clothing and implements, and can be programmed and activated remotely. We review existing vibrotactile technologies and known uses of vibrotactile stimuli; describe the hardware, textile, and software components of Haplos; describe results from a quasi-experimental workshop to evaluate Haplos; and discuss future research and development directions.

Many robot applications involve lifelong planning in relatively static environments e.g. assembling objects or sorting mail in an office building. In these types of scenarios, the robot performs many tasks over a long period of time. Thus, the time required for computing a motion plan becomes a significant concern, prompting the need for a fast and efficient motion planner. Since these environments remain similar in between planning requests, planning from scratch is wasteful. Recently, Experience Graphs (E-Graphs) were proposed to accelerate the planning process by reusing parts of previously computed paths to solve new motion planning queries more efficiently. This work describes a method to improve planning times with E-Graphs given changes in the environment by lazily evaluating the validity of past experiences during the planning process. We show the improvements with our method in a single-arm manipulation domain with simulations on the PR2 robot.

Experience Graphs have been shown to accelerate motion planning using parts of previous paths in an A* framework. Experience Graphs work by computing a new heuristic for weighted A* search on top of the domain's original heuristic and the edges in an Experience Graph. The new heuristic biases the search toward relevant prior experience and uses the original heuristic for guidance otherwise. In previous work, Experience Graphs were always built on top of domain heuristics which were computed by dynamic programming (a lower dimensional version of the original planning problem). When the original heuristic is computed this way the Experience Graph heuristic can be computed very efficiently. However, there are many commonly used heuristics in planning that are not computed in this fashion, such as euclidean distance. While the Experience Graph heuristic can be computed using these heuristics, it is not efficient, and in many cases the heuristic computation takes much of the planning time. In this work, we present a more efficient way to use these heuristics for motion planning problems by making use of popular nearest neighbor algorithms. Experimentally, we show an average 8 times reduction in heuristic computation time, resulting in overall planning time being reduced by 66%. with no change in the expanded states or resulting path.

Learning from demonstration (LfD) is a common technique applied to many problems in robotics, such as populating grasp databases, training for reinforcement learning of high-level skill sets and bootstrapping motion planners. While such approaches are generally highly valued, they rely on the often time-consuming process of gathering user demonstrations, and hence it becomes difficult to attain a sizeable dataset. In this paper, we present a tool capable of recording large numbers of high-dimensional demonstrations of mobile manipulation tasks provided by non-experts in the field. Our tool accomplishes this via a web interface that requires no additional software to be installed beyond a web browser, as well as a scalable architecture that is capable of supporting 10 concurrent demonstrators on a single server. Our architecture employs a lightweight simulation environment to reduce unnecessary computations and improve performance. Furthermore, we show how our tool can be used to gather a large set of demonstrations of a mobile manipulation task by leveraging existing crowdsource platforms. The data set collected has been made available to the robotics community. We also present experiments in which we apply demonstrations collected through our infrastructure to teach a robot how to grasp, to teach a robot how to perform dexterous manipulation tasks such as scooping and to accelerate motion planning for full-body manipulation tasks.

Follow-the-leader based approaches have been popular for the control of multi-robot teams for their ability to drive many with few. Typically, in these methods you select a single leader, generate a plan for it, while all other agents follow this leader using their individual controllers. However, there are many scenarios where this approach can lead to highly suboptimal behavior or even failure in the presence of clutter. In this work, we present a planning approach that automatically figures out when to switch leaders on the way to the goal while minimizing a given cost function that penalizes leader switching and deviations from the desired formation. To deal with the increased dimensionality of the problem we show how a recently developed algorithm, MHA* (multi-heuristic A*) can be extended to support planning for a team of robots. We also provide explicit cost minimization and guarantee that paths found are within a user- chosen factor of optimality with respect to the graph modeling the planning problem. Experimentally, we found that allowing for dynamic leader-switching leads to a significant increase in finding feasible plans for multi-robot teams ranging up to 21 robots.

Coastal zones are fragile and dynamic environments, most of the time largely urbanized and particularly vulnerable to natural hazards. Therefore, coastal areas are often exposed to high risk and shoreline position monitoring and modelling is required to mitigate it. In this context, satellite data are fundamental to provide synoptic and multitemporal information useful to map and model shoreline position through time. The aim of this work was to study the shoreline evolution of two selected areas, in Portugal and in Italy. Shoreline historical rates were obtained by analyzing Landsat images from mid-80's up to 2011. Subsequently, short-term scenarios (2014) were predicted and their accuracy was assessed by comparing 2014 modelled and observed shoreline positions. After that, Landsat 8 and Sentinel 1 images were exploited to extract and compare 2015 shoreline positions in a Data Fusion context. Finally, results were interpreted for their implications in the coastal risk reduction framework.

Many robotic systems are comprised of two or more arms. Such systems range from dual-arm household manipulators to factory floors populated with a multitude of industrial robotic arms. While the use of multiple arms increases the productivity of the system and extends dramatically its workspace, it also introduces a number of challenges. One such challenge is planning the motion of the arm(s) required to relocate an object from one location to another. This problem is challenging because it requires reasoning over which arms and in which order should manipulate the object, finding a sequence of valid handoff locations between the consecutive arms and finally choosing the grasps that allow for successful handoffs. In this paper, we show how to exploit the characteristics of this problem in order to construct a planner that can solve it effectively. We analyze our approach experimentally on a number of simulated examples ranging from a 2-arm system operating at a table to a 3-arm system working at a bar and to a 4-arm system in a factory setting.

Robots operating in real world environments need to find motion plans quickly. Robot motion should also be efficient and, when operating among people, predictable. Minimizing a cost function, e.g. path length, can produce short, reasonable paths. Anytime planners are ideal for this since they find an initial solution quickly and then improve solution quality as time permits. In previous work, we introduced the concept of Experience Graphs, which allow search-based planners to find paths with bounded sub-optimality quickly by reusing parts of previous paths where relevant. Here we extend planning with Experience Graphs to work in an anytime fashion so a first solution is found quickly using prior experience. As time allows, the dependence on this experience is reduced in order to produce closer to optimal solutions. We also demonstrate how Experience Graphs provide a new way of approaching incremental planning as they naturally reuse information when the environment, the starting configuration of the robot or the goal configuration change. Experimentally, we demonstrate the anytime and incremental properties of our algorithm on mobile manipulation tasks in both simulation and on a real PR2 robot.

Collision-free navigation in cluttered environments is essential for any mobile manipulation system. Traditional navigation systems have relied on a 2D grid map projected from a 3D representation for efficiency. This approach, however, prevents navigation close to objects in situations where projected 3D configurations are in collision within the 2D grid map even if actually no collision occurs in the 3D environment. Accordingly, when using such a 2D representation for planning paths of a mobile manipulation robot, the number of planning problems which can be solved is limited and suboptimal robot paths may result. We present a fast, integrated approach to solve path planning in 3D using a combination of an efficient octree-based representation of the 3D world and an anytime search-based motion planner. Our approach utilizes a combination of multi-layered 2D and 3D representations to improve planning speed, allowing the generation of almost real-time plans with bounded sub-optimality. We present extensive experimental results with the two-armed mobile manipulation robot PR2 carrying large objects in a highly cluttered environment. Using our approach, the robot is able to efficiently plan and execute trajectories while transporting objects, thereby often moving through demanding, narrow passageways.

Path planning in dynamic environments is significantly more difficult than navigation in static spaces due to the increased dimensionality of the problem, as well as the importance of returning good paths under time constraints. Anytime planners are ideal for these types of problems as they find an initial solution quickly and then improve it as time allows. In this paper, we develop an anytime planner that builds off of Safe Interval Path Planning (SIPP), which is a fast A*-variant for planning in dynamic environments that uses intervals instead of timesteps to represent the time dimension of the problem. In addition, we introduce an optional time-horizon after which the planner drops time as a dimension. On the theoretical side, we show that in the absence of time-horizon our planner can provide guarantees on completeness as well as bounds on the sub-optimality of the solution with respect to the original space-time graph. We also provide simulation experiments for planning for a UAV among 50 dynamic obstacles, where we can provide safe paths for the next 15 seconds of execution within 0.05 seconds. Our results provide a strong evidence for our planner working under real-time constraints.

In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph. On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on suboptimality with respect to the graph that represents the original planning problem. Experimentally, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for two armed mobile manipulation.

In a number of graph search-based planning problems, the value of the cost function that is being minimized also affects the set of possible actions at some or all the states in the graph. For example, in path planning for a robot with a limited battery power, a common cost function is energy consumption, whereas the level of remaining energy affects the navigational capabilities of the robot. Similarly, in path planning for a robot navigating dynamic environments, a total traversal time is a common cost function whereas the timestep affects whether a particular transition is valid. In such planning problems, the cost function typically becomes one of the state variables thereby increasing the dimensionality of the planning problem, and consequently the size of the graph that represents the problem. In this paper, we show how to avoid this increase in the dimensionality for the planning problems whenever the availability of the actions is monotonically non-increasing with the increase in the cost function. We present three variants of A* search for dealing with such planning problems: a provably optimal version, a suboptimal version that scales to larger problems while maintaining a bound on suboptimality, and finally a version that relaxes our assumption on the relationship between the cost function and action space. Our experimental analysis on several domains shows that the presented algorithms achieve up to several orders of magnitude speed up over the alternative approaches to planning.

Robotic path planning in static environments is a thoroughly studied problem that can typically be solved very efficiently. However, planning in the presence of dynamic obstacles is still computationally challenging because it requires adding time as an additional dimension to the search-space explored by the planner. In order to avoid the increase in the dimensionality of the planning problem, most real-time approaches to path planning treat dynamic obstacles as static and constantly re-plan as dynamic obstacles move. Although gaining efficiency, these approaches sacrifice optimality and even completeness. In this paper, we develop a planner that builds on the observation that while the number of safe timesteps in any configuration may be unbounded, the number of safe time intervals in a configuration is finite and generally very small. A safe interval is a time period for a configuration with no collisions and if it were extended one timestep in either direction, it would then be in collision. The planner exploits this observation and constructs a search-space with states defined by their configuration and safe interval, resulting in a graph that generally only has a few states per configuration. On the theoretical side, we show that our planner can provide the same optimality and completeness guarantees as planning with time as an additional dimension. On the experimental side, in simulation tests with up to 200 dynamic obstacles, we show that our planner is significantly faster, making it feasible to use in real-time on robots operating in large dynamic environments. We also ran several real robot trials on the PR2, a mobile manipulation platform.

A Mobile Virtual Assistant (MVA) is a communication agent that recognizes and understands free speech, and performs actions such as retrieving information and completing transactions. One essential characteristic of MVAs is their ability to learn and adapt without supervision. This paper describes our ongoing research in developing more intelligent MVAs that recognize and understand very large vocabulary speech input across a variety of tasks. In particular, we present our architecture for unsupervised acoustic and language model adaptation. Experimental results show that unsupervised acoustic model learning approaches the performance of supervised learning when adapting on 40-50 device-specific utterances. Unsupervised language model learning results in an 8% absolute drop in word error rate.

In a number of graph search-based planning problems, the value of the cost function that is being minimized also affects the set of possible actions at some or all the states in the graph. In such planning problems, the cost function typically becomes one of the state variables thereby increasing the dimensionality of the planning problem, and consequently the size of the graph that represents the problem. In this paper, we show how to avoid this increase in the dimensionality for weighted search (with bounded suboptimality) whenever the availability of the actions is monotonically non-increasing with the increase in the cost function.

Summary form only given. Over the past twenty years, we have seen an increasing number of successful deployments of spoken language technology. But, there is not a single market where these systems have become truly ubiquitous. In this talk, I will discuss the use of speech technology in mobile devices. Because of the particular features and constraints of these devices (personal device, increasing demand for applications and functionality, limited alternatives for text entry), it's looking like this is the place where we will see widespread adoption of speech technologies as one of the modes of interaction. I will discuss the current state of this industry and the things we need to do to create this widespread adoption.

VRML-based environments can be used very effectively for reaching a variety of online courses. This paper describes the development of an Internet-based collaborative learning environment in which VRML is not only the means but also the subject of teaching. Such a VRML environment is designed to assist and support employees of the 'new media' industries enrolled on short courses run by the Interactive Media Group in the School of Computing, University of Plymouth. This paper focuses on some key issues in the design of the VRML teaching environment and using it for real-time and on-demand course delivery. One of the most interesting issues is the experience of learning and teaching VRML while being within a VRML world. Such an immersive method of learning provides students with unique experiences and significantly increases the efficiency of the learning process.

The Carnegie-Mellon Speech Group is working with a number of institutions in the DARPA community to develop "A New Generation English Language System" (ANGELS) to perform large vocabulary speaker-independent recognition of natural continuous speech. A major focus of this effort is the development of an acoustic-phonetic module that provides an accurate phonetic transcription of an unknown utterance. This paper describes the phonetic classification system now under development, the research approach and some preliminary results.