Our ReFlex robotic hand and TakkTile sensor products have been used by researchers all over the world. Here is a glimpse of some of our customers as well as how they're using our ReFlex Hand technology.
CENTRE FOR THEORETICAL NEUROSCIENCE, UNIVERSITY OF WATERLOO
Project: Characterization of Grasping Decisions With a Human-Operated Gripper
People: Rajan Iyengar, Bryan Tripp
The lab's goal is to understand the information processing performed by the primate visual cortex. The group develops computational models of the visual cortex, and compares these models to neural recordings and human behavior. The team is particularly interested in the visual guidance of movement, including control of the eyes, hands, and body.
The group is developing an intuitive interface for direct human control of the ReFlex Hand, and is using this to study visually guided grasping decisions in humans.
GRASP LAB, UNIVERSITY OF PENNSYLVANIA
Project: A Triangle Histogram for Object Classification by Tactile Sensing
People: Mabel M. Zhang, Monroe D. Kennedy III, M. Ani Hsieh, Kostas Daniilidis
The lab uses the ReFlex Hand for tactile object recognition and active tactile sensing. Recent development in computer vision and tactile hardware has allowed tactile object recognition approaches to treat dense tactile array inputs as images and to apply standard vision feature descriptors. Such dense sensing arrays are costly. The ReFlex Hand provides affordable and compliant hardware to demonstrate the robustness of our object classification method, which uses sparse touch inputs and does not rely on highly accurate hand proprioception. The team aims to actively determine a minimal set of contact points to touch in order to identify an everyday object.
HARVARD BIOROBOTICS LAB, HARVARD UNIVERSITY
Project: Tactile-Sensing-Based Grasp Prediction and Refinement
People: Qian Wan, Ryan P. Adams, Robert D. Howe
The group's research focuses on using tactile information to enable and improve autonomous grasping and manipulation in unstructured environments.
For this project, the ReFlex hand is used for collecting tactile information during real, as opposed to simulated, grasping. The team has collected thousands and thousands of trials to use in our machine learning algorithms, and the hand has been very robust throughout.
INTELLIGENT AUTONOMOUS SYSTEMS, TU DARMSTADT°
UNIVERSITY OF UTAH ROBOTICS CENTER, UNIVERSITY OF UTAH°°
Project: Learning Robot In-Hand Manipulation with Tactile Features
People: Herke van Hoof°°, Tucker Hermans°, Gerhard Neumann°°, Jan Peters°°
Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. While this aim has been a long-standing vision of artificial intelligence and the cognitive sciences, we have yet to achieve the first step of creating robots that can learn to accomplish many different tasks triggered by environmental context or higher-level instruction. The goal of the robot learning laboratory is the realization of a general approach to motor skill learning, to get closer towards human-like performance in robotics. The team focuses on the solution of fundamental problems in robotics while developing machine-learning methods.
Exact object models are not available for unknown objects. Instead of relying on models, we exploit compliance and tactile feedback to adapt to unknown objects. However, compliant hands and tactile sensors are themselves difficult to model. Therefore, in-hand manipulation skills are acquired through reinforcement learning.
INTELLIGENT MOTION LAB, DUKE UNIVERSITY
Project: The Tele-Robotic Intelligent Nursing Assistant (TRINA)
People: Jane Li, Peter Moran, Carrina Dong, Ryan Shaw, Kris Hauser
During outbreaks of contagious diseases, healthcare workers are at high risk for infection due to routine interaction with patients, handling of contaminated materials, and challenges associated with safely removing protective gear. This project is developing the Tele-Robotic Intelligent Nursing Assistant (TRINA), a remote-controlled robot to address these challenges. Such robots could perform common nursing duties inside hazardous clinical areas, which could reduce infection risk to healthcare workers by minimizing exposure to contagions and other biohazards.
The group is investigating several aspects of the tele-robotic nursing problem, including intuitive input devices, improved contextual awareness, and operator assistance algorithms that automate or partially-automate tedious and error-prone tasks. External collaborators are also studying the use of mobile sensors for providing rich information to the nurse, methods for enhancing the robot's manipulation capabilities, and designing disposable protective coverings.