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The results show that replay retention depends on change magnitude and on how the dynamics evolve, and an estimator built from interaction data can still provide the quantities needed to choose a replay strategy after permanent changes.
Achieving a staggering 90.4% reduction in robot-motion error, Hydra-0 redefines how we model and control robotic actions across varied environments.
Robots can now learn complex skills more efficiently, achieving optimal performance even with limited practice time.
Achieving 98.1% of oracle performance in surgical tissue retraction planning with only 40 noisy observations could revolutionize autonomous surgical systems.
A groundbreaking dataset reveals that 3D particle models significantly outperform 2D video models in capturing the complexities of deformable object dynamics.
Achieve effective task planning with missing objects by interleaving search and execution, outperforming learned baselines in simulated and real-world environments.
