In this paper we introduce STEPPR (Sandia Transmission-Efficient Prototype Promoting Research), a bipedal robot designed to explore efficient bipedal walking. The initial iteration of this robot achieves efficient motions through powerful electromagnetic actuators and highly back-drivable synthetic rope transmissions. We show how the addition of parallel elastic elements at select joints is predicted to provide substantial energetic benefits: reducing cost of transport by 30 to 50 percent. Two joints in particular, hip roll and ankle pitch, reduce dissipated power over three very different gait types: human walking, human-like robot walking, and crouched robot walking. Joint springs based on this analysis are tested and validated experimentally. Finally, this paper concludes with the design of two unique parallel spring mechanisms to be added to the current STEPPR robot in order to provide improved locomotive efficiency.

This paper presents a novel approach to identify chile pods in two dimensional images of chile harvests in order to quantitatively evaluate harvesting and cleaning systems. Chilies vary greatly in shape, color, and texture throughout the season and within varieties and thus pose a unique challenge compared to the identification of other more uniform objects. Since chilies do not have distinct point features typically exploited in object recognition algorithms, a normalized shape distribution function was used to describe each object's general shape. 486 harvest object images were collected throughout the season and then placed in categories. Representative shape functions were derived from chilies in a given category and the other chilies, stems, and leaves were compared to each class. Sample shape distributions were found to provide a robust signature to discriminate among chile pods and harvest trash objects.