Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand, big robotics companies offer training programs based on the job requirements. However, these training programs may not be as beneficial as general robotics programs offered by universities or community colleges. On the other hand, community colleges and universities face challenges with required resources, especially qualified instructors, to offer students advanced robotics education. Furthermore, the diverse backgrounds of undergraduate students present additional challenges. Some students bring extensive industry experiences, while others are newcomers to the field. To address these challenges, we propose a student-centered personalized learning framework for robotics. This framework allows a general instructor to teach undergraduate-level robotics courses by breaking down course topics into smaller components with well-defined topic dependencies, structured as a graph. This modular approach enables students to choose their learning path, catering to their unique preferences and pace. Moreover, our framework's flexibility allows for easy customization of teaching materials to meet the specific needs of host institutions. In addition to teaching materials, a frequently-asked-questions document would be prepared for a general instructor. If students' robotics questions cannot be answered by the instructor, the answers to these questions may be included in this document. For questions not covered in this document, we can gather and address them through collaboration with the robotics community and course content creators. Our user study results demonstrate the promise of this method in delivering undergraduate-level robotics education tailored to individual learning outcomes and preferences.

Machine learning models pre-trained on large datasets have achieved remarkable convergence and robustness properties. However, these models often exploit spurious correlations between certain attributes and labels, which are prevalent in the majority of examples within specific categories but are not predictive of these categories in general. The learned spurious correlations may persist even after fine-tuning on new data, which degrades models' performance on examples that do not exhibit the spurious correlation. In this work, we propose a simple and highly effective method to eliminate spurious correlations from pre-trained models. The key idea of our method is to leverage a small set of examples with spurious attributes, and balance the spurious attributes across all classes via data mixing. We theoretically confirm the effectiveness of our method, and empirically demonstrate its state-of-the-art performance on various vision and NLP tasks, including eliminating spurious correlations from pre-trained ResNet50 on Waterbirds and CelebA, adversarially pre-trained ResNet50 on ImageNet, and BERT pre-trained on CivilComments.