Juan Rojas

Learn More
— Snap assembly automation remains a challenging task. While progress is being made in localization of parts, force controllers, and control strategies, little work has been done to help the robot reason about its current state, such that if necessary, the robot can assume corrective actions to accomplish the task. Error prone situations caused by the(More)
—Autonomous snap assemblies is a highly desirable robotic functionality. While much work has been done in active sensing for peg-in-hole assemblies and general compliant motions, snap assembly state estimation remains an open research problem. This work presents a probabilistic framework designed to account for uncertainties in assembly and yield more(More)
— In this work a gradient calibration method was presented as part of the Relative-Change-Based-Hierarchical Taxonomy (RCBHT) cantilever-snap verification system and the Pivot Approach control strategy for the automation of cantilever-snaps. As part of a relative-change based force signal interpretation scheme, an effective gradient calibration process is(More)
— Failure detection and correction is essential in robust systems. In robotics, failure detection has focused on traditional parts assembly, tool breakage, and threaded fastener assembly. However, not much work has focused on sub-mode failure classification. This is an important step in order to provide accurate failure recovery. Our work implemented a(More)
— Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define state transitions in a Markov chain or finite state machines. Such design is prone to failure in unstructured environments, when due to external(More)
— Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan-act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a(More)