Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure

A Novel Azeotropic Mixture for Solvent Extraction of Edible Oils

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 8 (2006): A Novel Azeotropic Mixture for Solvent Extraction of Edible Oils. Manuscript FP 06 005. Vol. VIII. April, 2006

To Fall Or Not To Fall: {A} Visual Approach to Physical Stability Prediction

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli

Understanding images in biological and computer vision

yesThis issue of Interface Focus is a collection of papers arising out of a Royal Society Discussion meeting entitled ‘Understanding images in biological and computer vision’ held at Carlton Terrace on the 19th and 20th February, 2018. There is a strong tradition of inter-disciplinarity in the study of visual perception and visual cognition. Many of the great natural scientists including Newton [1], Young [2] and Maxwell (see [3]) were intrigued by the relationship between light, surfaces and perceived colour considering both physical and perceptual processes. Brewster [4] invented both the lenticular stereoscope and the binocular camera but also studied the perception of shape-from-shading. More recently, Marr's [5] description of visual perception as an information processing problem led to great advances in our understanding of both biological and computer vision: both the computer vision and biological vision communities have a Marr medal. The recent successes of deep neural networks in classifying the images that we see and the fMRI images that reveal the activity in our brains during the act of seeing are both intriguing. The links between machine vision systems and biology may at sometimes be weak but the similarity of some of the operations is nonetheless striking [6]. This two-day meeting brought together researchers from the fields of biological and computer vision, robotics, neuroscience, computer science and psychology to discuss the most recent developments in the field. The meeting was divided into four themes: vision for action, visual appearance, vision for recognition and machine learning

Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database

Selecting the most suitable local invariant feature detector for a particular application has rendered the task of evaluating feature detectors a critical issue in vision research. Although the literature offers a variety of comparison works focusing on performance evaluation of image feature detectors under several types of image transformations, the influence of the scene content on the performance of local feature detectors has received little attention so far. This paper aims to bridge this gap with a new framework for determining the type of scenes which maximize and minimize the performance of detectors in terms of repeatability rate. The results are presented for several state-of-the-art feature detectors that have been obtained using a large image database of 20482 images under JPEG compression, uniform light and blur changes with 539 different scenes captured from real-world scenarios. These results provide new insights into the behavior of feature detectors

Introducing wearable haptics for rendering velocity feedback in VR serious games for neuro-rehabilitation of children

Rehabilitation in virtual reality offers advantages in terms of flexibility and parametrization of exercises, repeatability, and continuous data recording and analysis of the progress of the patient, also promoting high engagement and cognitive challenges. Still, most of the proposed virtual settings provide a high quality, immersive visual and audio feedback, without involving the sense of touch. In this paper, we show the design, implementation, and first evaluation of a gaming scenario for upper limb rehabilitation of children with cerebral palsy. In particular, we took care to introduce haptic feedback as a useful source of sensory information for the proposed task, considering—at the same time—the strict constraints for haptic wearable devices to comply with patient’s comfort, residual motor abilities, and with the embedded tracking features of the latest VR technologies. To show the potential of haptics in a rehabilitation setup, the proposed device and rendering method have been used to improve the velocity control of upper limb movements during the VR exercise, given its importance as a motor recovery metric. Eight healthy participants were enrolled, and results showed that haptic feedback can lead to lower speed tracking errors and higher movement smoothness, making the proposed setup suitable to be used in a rehabilitation context as a way to promote movement fluidity during exercises

Immersive Virtual Environments and Wearable Haptic Devices in rehabilitation of children with neuromotor impairments: a single-blind randomized controlled crossover pilot study

Background: The past decade has seen the emergence of rehabilitation treatments using virtual reality. One of the advantages in using this technology is the potential to create positive motivation, by means of engaging environments and tasks shaped in the form of serious games. The aim of this study is to determine the efficacy of immersive Virtual Environments and weaRable hAptic devices (VERA) for rehabilitation of upper limb in children with Cerebral Palsy (CP) and Developmental Dyspraxia (DD). Methods: A two period cross-over design was adopted for determining the differences between the proposed therapy and a conventional treatment. Eight children were randomized into two groups: one group received the VERA treatment in the first period and the manual therapy in the second period, and viceversa for the other group. Children were assessed at the beginning and the end of each period through both the Nine Hole Peg Test (9-HPT, primary outcome) and Kinesiological Measurements obtained during the performing of similar tasks in a real setting scenario (secondary outcomes). Results: All subjects, not depending from which group they come from, significantly improved in both the performance of the 9-HPT and in the parameters of the kinesiological measurements (movement error and smoothness). No statistically significant differences have been found between the two groups. Conclusions: These findings suggest that immersive VE and wearable haptic devices is a viable alternative to conventional therapy for improving upper extremity function in children with neuromotor impairments. Trial registration ClinicalTrials, NCT03353623. Registered 27 November 2017-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03353623

Seed morphobiometry of wild and cultivated taxa of Phaseolus L. (Fabaceae)

A morphobiometric analysis of seeds of Phaseolus L. from 50 populations belonging to 31 wild and cultivated taxa was carried out. Based on the outcome of the study an identifi cation key was developed comprising 25 morphotypes of which 23 related to individual taxa The different patterns of seminal tegument allowed 31 taxa to cluster into three groups: (1) Phaseolus angustissimus Gray group (wrinkled seed coat) with two morphotypes, (2) Phaseolus lunatus L. group (smooth tegument with striae) with ten morphotypes and (3) Phaseolus vulgaris L. group (smooth tegument without striae) with 13 morphotypes. All the taxa exhibited uniformity in size and variability in tegument colour of seeds irrespective of the source of population and the type of habitat. Characterization of taxa into defi nite morphotypes and the groups could be useful for biosystematic investigations and the markerbased genetic selection approaches in this important leguminous crop