A situated cognition perspective on presence

During interaction with computer-based 3-D simulations like virtual reality, users may experience a sense of involvement called presence. Presence is commonly defined as the subjective feeling of "being there". We discuss the state of the art in this inno vative research area and introduce a situated cognition perspective on presence. We argue that presence depends on the proper integration of aspects relevant to an agent's movement and perception, to her actions, and to her conception of the overall situ a tion in which she finds herself, as well as on how these aspects mesh with the possibilities for action afforded in the interaction with the artifact. We also aim at showing that studies of presence offer a test-bed for different theories of situated co gnition.

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Affordances in Psychology, Neuroscience, and Robotics: A Survey

The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule

User Experience-driven Innovation in Smart and Connected Worlds

In our fast-paced digital economy, expectations for improved user experiences (UX) increasingly drive innovation. Thus, companies must fully grasp users’ points of view when they design innovative technologies that can successfully compete in a crowded global market. These technologies must not only satisfy users’ expectations but also empower them and improve their quality of life. To address this challenge in our rapidly evolving and globally expanding digital economy, we need new theories and models for technology design—ones that incorporate UX. In this paper, we address this need by developing conceptual models for UX-driven innovation. We explain how these models can enable innovative, responsive technologies that meet users’ needs in real time. These models also facilitate the production of new theories that are discovered via accessing the rich, real-time data sets that our increasingly smart and connected worlds create

Similarity Reasoning over Semantic Context-Graphs

Similarity is a central cognitive mechanism for humans which enables a broad range of perceptual and abstraction processes, including recognizing and categorizing objects, drawing parallelism, and predicting outcomes. It has been studied computationally through models designed to replicate human judgment. The work presented in this dissertation leverages general purpose semantic networks to derive similarity measures in a problem-independent manner. We model both general and relational similarity using connectivity between concepts within semantic networks. Our first contribution is to model general similarity using concept connectivity, which we use to partition vocabularies into topics without the need of document corpora. We apply this model to derive topics from unstructured dialog, specifically enabling an early literacy primer application to support parents in having better conversations with their young children, as they are using the primer together. Second, we model relational similarity in proportional analogies. To do so, we derive relational parallelism by searching in semantic networks for similar path pairs that connect either side of this analogy statement. We then derive human readable explanations from the resulting similar path pair. We show that our model can answer broad-vocabulary analogy questions designed for human test takers with high confidence. The third contribution is to enable symbolic plan repair in robot planning through object substitution. When a failure occurs due to unforeseen changes in the environment, such as missing objects, we enable the planning domain to be extended with a number of alternative objects such that the plan can be repaired and execution to continue. To evaluate this type of similarity, we use both general and relational similarity. We demonstrate that the task context is essential in establishing which objects are interchangeable

Brain Rhythms in Object Recognition and Manipulation

Our manual interactions with objects represent the most fundamental activity in our everyday life. Whereas the grasp of an object is driven by the perceptual senses, using an object for its function relies on learnt experience to retrieve. Recent theories explain how the brain takes decisions based on perceptual information, yet the question of how does it retrieve object knowledge to use tools remains unanswered. Discovering the neuronal implementation of the retrieval of object knowledge would help understanding praxic impairments and provide appropriate neurorehabilitation. This thesis reports five investigations on the neuronal oscillatory activity involved in accessing object knowledge. Employing an original paradigm combining EEG recordings with tool use training in virtual reality, I demonstrated that beta oscillations are crucial to the retrieval of object knowledge during object recognition. Multiple evidence points toward an access to object knowledge during the 300 to 400 ms of visual processing. The different topographies of the beta oscillations suggest that tool knowledge is encoded in distinct brain areas but generally located within the left hemisphere. Importantly, learning action information about an object has consequences on its manipulations. Multiplying tool use knowledge about an object increases the beta desynchronization and slows down motor control. Furthermore, the present data report an influence of language on object manipulations and beta oscillations, in a way that learning the name of an object speeds up its use while impedes its grasp. This shred of evidence led to the formulation of three testable hypotheses extending contemporary theories of object manipulation and semantic memory. First, the preparation of object transportation or use could be distinguished by the synchronization/desynchronization patterns of mu and beta rhythms. Second, action competitions originate from both perceptuo-motor and memory systems. Third, accessing to semantic object knowledge during object processing could be indexed by the bursts of desynchronization of high-beta oscillations in the brain.MSCA-ETN SECURE [642667

Supporting students with learning disabilities to explore linear relationships using online learning objects

The study of linear relationships is foundational for mathematics teaching and learning. However, students’ abilities connect different representations of linear relationships have proven to be challenging. In response, a computer-based instructional sequence was designed to support students’ understanding of the connections among representations. In this paper we report on the affordances of this dynamic mode of representation specifically for students with learning disabilities. We outline four results identified by teachers as they implemented the online lessons