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Cornell's RHyME Framework Revolutionizes Robot Learning with Single Video Demonstrations

The AI-driven system enables robots to master multi-step tasks with minimal training data, leveraging memory and mismatch handling for adaptability.

Kushal Kedia (left), a doctoral student in the field of computer science, and Prithwish Dan, M.S. ’26, are members of the development team behind RHyME, a system that allows robots to learn tasks by watching a single how-to video.
image : ©JK1991 | iStock
RHyME is the team’s answer – a scalable approach that makes robots less finicky and more adaptive. It supercharges a robotic system to use its own memory and connect the dots when performing tasks it has viewed only once by drawing on videos it has seen. Credit: Neuroscience News

Overview

  • RHyME, short for Retrieval for Hybrid Imitation under Mismatched Execution, allows robots to learn tasks by watching a single how-to video.
  • The framework requires just 30 minutes of robot training data and achieves over 50% higher task success rates compared to earlier methods.
  • RHyME uses a memory bank of previously seen videos to adapt to new tasks, bridging differences between human and robot motions.
  • Unlike traditional systems, RHyME handles mismatches in human-robot execution, enabling flexible and robust learning.
  • The research, supported by Google, OpenAI, and U.S. government agencies, will be presented at the IEEE Robotics Conference in May 2025.