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.

A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

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 new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health data and fashion designs.

“MouthIO” is an in-mouth device that users can digitally design and 3D print with integrated sensors and actuators to capture health data and interact with a computer or phone.

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.