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Robotics education is often constrained by the high cost and limited accessibility of physical robots, which can hinder the learning experience for many students. To address this challenge, the Fundamentals of Robotics Education (FORE) project, part of a larger NSF-funded collaborative work, was developed to create an accessible and comprehensive online learning platform. FORE provides a student-centered approach to robotics education, featuring a robust code editor, real-time simulation, and interactive lessons. This paper presents the architecture and implementation of the FORE platform, highlighting its key components, including the backend simulation using Gazebo and ROS2, a frontend visualizer built with Three.js, and the integration of a Python-based coding environment. We discuss the development process, the contributions of the student team, and the challenges encountered during the project. The results demonstrate the platform’s effectiveness in making robotics education more easily available. These findings originate from software testing and utilization by senior computer science students, as well as feedback from participants at the University of Nevada, Reno College of Engineering’s annual Capstone Course Innovation Day. The platform allows students to gain hands-on experience without the need for physical hardware. Its adaptability enables it to serve a broad audience of undergraduate students, offering an encompassing and accessible solution for modern robotics education. 

Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness. However, estimating physical material properties is an open problem due to the lack of material ground-truth data, as measuring these properties for real objects is highly difficult. We present PhysDreamer, a physics-based approach that endows static 3D objects with interactive dynamics by leveraging the object dynamics priors learned by video generation models. By distilling these priors, PhysDreamer enables the synthesis of realistic object responses to novel interactions, such as external forces or agent manipulations. We demonstrate our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study. PhysDreamer takes a step towards more engaging and realistic virtual experiences by enabling static 3D objects to dynamically respond to interactive stimuli in a physically plausible manner. See our project page at this https URL. 

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 the 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 experience, 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. 

We present a new chemical mechanism for Hg(0)/ Hg(I) / Hg(II) atmospheric cycling, including recent laboratory and computational data, and implement it in the GEOS-Chem global atmospheric chemistry model for comparison to observations. Our mechanism includes the oxidation of Hg(0) by Br atoms and OH radicals, with subsequent oxidation of Hg(I) by ozone and radicals, re-speciation of gaseous Hg(II) in aerosols and cloud droplets, and speciated Hg(II) photolysis in the gas and aqueous phases. The tropospheric Hg lifetime against deposition in the model is 5.5 months, consistent with observational constraints. The model reproduces the observed global surface Hg(0) concentrations and Hg(II) wet deposition fluxes. Br and OH make comparable contributions to global net oxidation of Hg(0) to Hg(II). Ozone is the principal Hg(I) oxidant, enabling the efficient oxidation of Hg(0) to Hg(II) by OH. BrHgOH and Hg(OH)2 are the initial Hg(II) products of Hg0 oxidation, re-speciate in aerosols and clouds to organic and inorganic complexes, and volatilize to photostable forms. Reduction of Hg(II) to Hg(0) takes place largely through photolysis of aqueous Hg(II)-organic complexes. 71% of model Hg(II) deposition is to the oceans. Major mechanism uncertainties for atmospheric Hg chemistry modeling include the concentrations of Br atoms, the stability and reactions of Hg(I), and the speciation of Hg(II) in aerosols and clouds with implications for photoreduction.