Most undergraduates are not equipped to manage the unique challenges in developing software for modern robots, despite rapid expansion of the field. We here introduce a course we have designed and delivered to better prepare students to develop software for robot systems. It emphasizes the distinctive challenges of software development…
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Adequately covering the behaviors of autonomous vehicles ($AV$) is fundamental in their validation. However, quantifying such coverage is challenging as the $AV$s’ behavior is influenced by its physical environment that is often large and highly complex. This work builds on the insights that data sensed by the $AV$ provides a…
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Adversarial testing tends to focus on DNNs in isolation, to the exclusion of the full system state and system behaviors resulting from sequences of DNN output. In this work we propose a more holistic approach to DNN testing that accounts for the effects of perturbations on the system state. Our…
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Complex environments can cause robots to fail. Identifying the key elements of the environment associated with such failures is critical for faster fault isolation and, ultimately, debugging those failures. In this work we present the first automated approach for reducing the environment in which a robot failed. Similar to software…
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Probabilistic invariants can encode a family of conditional patterns, are generated using Bayesian inference to leverage observed trace data against priors gleaned from previous experience and expert knowledge, and are ranked based on their surprise value and information content. Our studies on two semi-autonomous mobile robotic systems show how the…
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Robotics utilizes algorithms for control, localization, and navigation that rely heavily on complex data structures such as matrices and graphs. The values of these data structures are the result of interleaved controllers and complex interactions between equations that are difficult to parameterize as they relate to observed behavior. This makes…
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