Children, Humanoid Robots and Caregivers

This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development

GAGAN: Geometry-Aware Generative Adversarial Networks

Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their texture, the visual appearance of objects is significantly influenced by their shape geometry; information which is not taken into account by existing generative models. This paper introduces the Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating geometric information into the image generation process. Specifically, in GAGAN the generator samples latent variables from the probability space of a statistical shape model. By mapping the output of the generator to a canonical coordinate frame through a differentiable geometric transformation, we enforce the geometry of the objects and add an implicit connection from the prior to the generated object. Experimental results on face generation indicate that the GAGAN can generate realistic images of faces with arbitrary facial attributes such as facial expression, pose, and morphology, that are of better quality than current GAN-based methods. Our method can be used to augment any existing GAN architecture and improve the quality of the images generated

Freehand Sketching for Engineers: A Pilot Study

This paper describes a pilot study to evaluate Freehand Sketching for Engineers, a one credit, five week course taught to undergraduate engineering students. The short-term goal of this course was to improve engineering students’ freehand sketching ability and to assess their progress with metrics. The long-term objective (desired learning outcome) of this course is to improve the creativity and innovation of student design projects by enhancing students’ ability to visualize their ideas with freehand sketches. The class met two days a week for 75 min per day. Students were taught to draw simple objects such as electrical boxes, with orthographic, isometric, and oblique views on 8 ½ x 11 in. sheets of blank paper (no grid lines) and wooden #2 pencils. No instruments, such as rulers and compasses, were allowed. The course required students to apply what they learned in the classroom and included many examples of hands-on, active and student-centered learning activities. Two assessments were performed to measure whether students improved their ability to freehand sketch. The first involved two outside reviewers (industrial designers) who evaluated each student’s sketch of a pipe fitting that was drawn in the first class (pre-test) and a sketch of the same pipe fitting in the eighth class (after 7 hours of instruction - post-test). Sketches were evaluated using a 1 (poor) to 7 (excellent) Likert scale. The second assessment consisted of an evaluation of the final projects, which were a collection of five sketches with different views of an engineered product. Evaluations of the pre- and post-test drawings and the final projects by outside reviewers and positive observations by engineering faculty suggest that this course has the potential to improve students’ ability to sketch objects. This paper discusses details of the course, provides examples of student sketches, and presents results of outside reviewer assessments. It includes suggestions for a more rigorous assessment of the course to determine its potential to improve students’ ability to sketch objects

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175

The Whole World in Your Hand: Active and Interactive Segmentation

Object segmentation is a fundamental problem in computer vision and a powerful resource for development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided by the presence of a hand or arm in the proximity of the object to be segmented. The first approach is suitable for a robotic system, where the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's perspective, with instrumentation to help detect and segment objects that are held in the wearer's hand. The third method operates when observing a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it as a seed for segmentation. We show that object segmentation can serve as a key resource for development by demonstrating methods that exploit high-quality object segmentations to develop both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities (object recognition and localization)