Squishy Robotics AI for Good: Machine Learning and Data Science for Deployable Robot-Driven Sensing and Detection of Methane Leaks
Alice Agogino, Professor
Mechanical Engineering
Applications for Fall 2026 are closed for this project.
Description: Squishy robots are rapidly deployable mobile sensing robots for disaster rescue, remote monitoring and space exploration. Our emergent technologies are at the fusion of robotics, mobile sensing, machine learning, big data fusion and smart IoT (Internet of Things).
Our first target market was the HazMat and CBRNE (Chemical, biological, radiological, nuclear and explosive) response market, enabling life-saving maneuvers and securing the safety of first responders by providing situational awareness and sensor data in uncharted terrain. This product can be deployed with multiple – even swarms – of collaborative squishy robots, equipped with visual, audio, chemical, biological, radiological and GPS sensors, that can traverse rough environments and be quickly deployed by ground or aerial vehicle to inform first responders, and assist in the rescue of victims until human first responders can arrive.
Our customizable platform is currently being designed for dynamically changing situations with a focus this year on methane monitoring and characterizations.
In this project, you will be collaborating with a team of academic researchers - M.DevEng students, postdoctoral researchers, and faculty - along with the Squishy Robotics Team. Your responsibility will involve investigation, adaptation and application of machine learning models to classify and ultimately predict the presence, occurrence, and dynamics of methane leaks. Your work will involve combining multiple scales and sources of data, demonstrating the improvement in model performance based on additional sensors (which would be deployed by Squishy Robots in field applications), and writing scholarly research reports related to your technical work.
Role: Your key work will involve the following:
- Reading, understanding, summarizing, and extracting key actionable insights, models, and codebase/datasets from leading-edge papers in the broad fields of machine learning
- Identifying, gathering, manipulating, cleaning and managing relevant datasets
- Recreating state-of-the-art results in the field of machine learning for climate change (methane leaks)
- Collaboratively coding, troubleshooting, applying, and characterizing machine learning models
- Visualizing and reporting results
Qualifications: Students should possess skills in at least one of the following, and strong interest in the others:
Experience with software, coding, and data science skills, especially related to applying machine learning models (e.g., Python, TensorFlow, Google CoLab)
Experience with data wrangling and management
Interest and motivation to read research papers in a variety of fields describing AI / ML models
Data visualization and reporting (e.g. Pandas, R, or other quantitative data visualization interest)
Interest and motivation to address climate change and disaster management via emerging technologies
Day-to-day supervisor for this project: Kseniya Kapytouskaya, Staff Researcher
Hours: to be negotiated
Off-Campus Research Site: Weekly mandatory, remote, and synchronous meetings will form the basis of collaboration, and students are expected to collaborate and meet among themselves more frequently. Much of the work will be done remotely or in the BEST (Berkeley Emergent Space Tensegrities) Lab on campus.
Related website: https://best.berkeley.edu/
Related website: http://squishy-robotics.com/