Human partners working on a target acquisition task perform faster than do individuals on the same task, even though the partners consider each other to be an impediment. We recorded the force profile of each partner during the task, revealing an emergent specialization of roles that could only have been negotiated through a haptic channel. With this understanding of human haptic communication we attempted a "haptic Turing test," replicating human behaviors in a robot partner. Human participants consciously and incorrectly believed their partner was human. However, force profiles did not show specialization of roles in the human partner, nor enhanced dyadic performance, suggesting that haptic interaction holds a greater subconscious subtlety. We further report observations of a non-zero dyadic steady state force perhaps analogous to co-contraction within the limb of an individual, where it contributes to limb stiffness and disturbance rejection. We present results on disturbance rejection in a dyad, showing lack of an effective dyadic strategy for brief events.

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Physical Collaboration of Human-Human and Human-Robot Teams

Human-robot social interaction plays an important role in spreading the use of the robot in human daily life. Through effective social interaction, robots will be able to perform many tasks in the human society. These tasks may include, but not limited to, handling various house duties, providing medical care for elderly people, assisting people with motor or cognitive disabilities, educational entertainment (edutainment), personal assistance, giving directions at information points in public places, etc. These applications need to develop social robots that are able to behave with humans as partners if not peers. This paper presents Maggie, a robotic platform developed at RoboticsLab for research on human-robot social interaction. The different developed interaction modules are also described.

Maggie: A Robotic Platform for Human-Robot Social Interaction

This article provides an overview of current techniques for dense geometric correspondence estimation. We first formally define geometric correspondence and investigate the different types of image pairs. Then, we briefly look at the classic approaches to correspondence estimation, at their feasibility and flaws for simultaneous dense estimation. We focus on the Bayesian approach, which is very well suited for this task, and for which several promising algorithms have previously been developed.

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Correspondence estimation in image pairs

Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human–robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.

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“Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot

Methods and systems for obtaining a motion vector between two frames of video image data are disclosed. Specifically, methods and systems of the present invention may be used to estimate a motion vector for each macroblock of a current frame with respect to a reference frame in a multi-stage operation. In a first stage, an application implementing the process of the present invention coarsely searches a first search area of the reference frame to obtain a candidate supermacroblock that best approximates a supermacroblock in the current frame (312). In a second stage, the supermacroblock is divided into a plurality of macroblocks (314). Each of the macroblocks is used to construct search areas which are then searched to obtain a candidate macroblock that best appropriates a macroblock in the current frame (322). Additional stages may be used to further fine-tune the approximation to the macroblock. The methods and systems of the present invention may be used in optimizing digital video encoders, decoders, and video format converters.

Hierarchical motion estimation process and system using block-matching and integral projection

The paper focuses on a new method for block based disparity estimation (BBDE) that is specially suited for real time coding of stereo sequences. The estimation is performed by exploiting a preliminary disparity field, obtained from the coarsest level of a multiresolution pyramid, and its successive refinements in the finer ones. A strong correction strategy for wrong estimates has been implemented by using an appropriate propagation of disparity vectors from one level to the next one and an adaptive search range. Finally, a variable-resolution disparity field is achieved stopping the propagation at an intermediate level of resolution, if a vector allows a good reconstruction quality of its block.

Hierarchical block matching for disparity estimation in stereo sequences

Applying machine learning techniques can help mobile robots meet the need for increased safety and adaptivity that real world operation demands. The techniques also facilitate robot to user communication. Using these techniques, we built increasingly abstract representations of a robot's perceptions and actions. This produced a symbolic description of what the robot knows and can do. Because this task is fairly complex, we first identified those subproblems that a learning method can solve efficiently, and isolated those with good classical solutions. Also, for a robot to solve a complex problem, we had to find solutions for several learning tasks. We identified these learning tasks and the learning techniques appropriate for their solution. To evaluate our approach, we used the mobile robots Priamos and Teseo. >

Using machine learning techniques in real-world mobile robots

Edge detection by point classification of Canny filtered images

This paper presents a progressive image coding technique in which a rough preview image is enhanced with the zerotree encoding of the residual image in the wavelet domain The preview image is achieved with a region based approach that gives a basic description of the scene with a very high compression ratio As a second stage, the residual between the original image and the preview is decomposed using a wavelet transform, and it is encoded for a progressive transmission The wavelet coefficients are selected from the coarsest to the finest level of resolution, using a mask obtained from the image preview Such a mask sets the importance of the coefficients on the basis of their distance from the contours of the regions of the preview A modified zerotree approach is used to progressively encode the weighted coefficients until significant texture is introduced in the image and the required quality is achieved Moreover, if an image with very good quality is required, the resulting reconstruction error is encoded by means of vector quantization and transmitted over the channel The proposed method is compared to progressive JPEG, and it gives similar results at low compression ratios and far better results at higher ones

Hierarchical progressive image coding controlled by a region based approach

A hierarchical block-based motion estimation strategy for video sequence coding is proposed, which allows to reduce both the computational load and the number of transmitted motion vectors. The estimation is performed by first computing a coarse motion field at the lowest resolution of a Gaussian pyramid and then propagating and refining it till the maximum resolution. the main innovations concern the adaptive vector propagation and updating strategy, which allow an efficient recovery from wrong estimates while maintaining a low computational load. The advantages of the proposed method are manifold: first, it is able to decrease the computation of even two orders magnitude for an equivalent compensation; second, it generates a lower number of motion vectors, resulting in a lower information rate; third, it produces a more homogeneous motion field, thus easing a successive segmentation.

M. Accame

Papers

Hierarchical block matching for disparity estimation in stereo sequences

Using machine learning techniques in real-world mobile robots

Edge detection by point classification of Canny filtered images

Hierarchical progressive image coding controlled by a region based approach

Hierarchical motion estimator (HME) for block-based video coders