New training approach could help AI agents perform better in uncertain conditions
Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.
Expanding robot perception
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
This fast and agile robotic insect could someday aid in mechanical pollination
With a new design, the bug-sized bot was able to fly 100 times longer than prior versions.
How humans continuously adapt while walking stably
Research could help improve motor rehabilitation programs and assistive robot control.
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
Daniela Rus wins John Scott Award
MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.
Creating innovative health solutions for individuals and populations
Physician and engineer Giovanni Traverso found an early passion for molecular genetics, leading to an interdisciplinary career helping others.
Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.
A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Combining next-token prediction and video diffusion in computer vision and robotics
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
Helping robots zero in on the objects that matter
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
For developing designers, there’s magic in 2.737 (Mechatronics)
Mechatronics combines electrical and mechanical engineering, but above all else it’s about design.
Engineering and matters of the heart
Professor Ellen Roche is creating the next generation of medical devices to help repair hearts, lungs, and other tissues.
AI assistant monitors teamwork to promote effective collaboration
An AI team coordinator aligns agents’ beliefs about how to achieve a task, intervening when necessary to potentially help with tasks in search and rescue, hospitals, and video games.