Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.

JFLAP is an educational software tool for experimenting with several types of automata, grammars and proofs related to the area of theoretical computer science. For example, one can create an NFA, convert it to a DFA, minimize the number of states in the DFA, convert it to a regular expression or convert it to a regular grammar. Once created, one can simulate test strings. JFLAP includes a large number of topics and proofs to experiment with from the regular languages, context-free languages, recursively enumerable languages and l-systems. This poster describes several new features in a redesign of JFLAP that includes a more flexible grammar representation, explicit formal definitions, basic set theory tools, and new algorithms.