SoftZoo is a soft robot co-design platform that can test optimal shapes and sizes for robotic performance in different environments.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
Digital twins to expand training capabilities through virtual reality.
MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
In collaboration with industry representatives, Momentum students tackle wildfire suppression and search-and-rescue missions while building soft skills.
“Evolution Gym” is a large-scale benchmark for co-optimizing the design and control of soft robots that takes inspiration from nature and evolutionary processes.
Animators spend hours adding textures to objects. A new machine-learning system simplifies the process.
New work on 2D and 3D meshing aims to address challenges with some of today’s state-of-the-art methods.
Teaching assistants in Robotics: Science and Systems pulled out all the stops to help engineering students race across the finish line this spring.