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Real-time turn-taking detection can be achieved with unprecedented accuracy and low latency using a novel dual-head modeling approach.
Robots can learn new skills from just one video in under 30 seconds while retaining what they've already mastered鈥攖ransforming the landscape of robotic skill acquisition.
By redefining action learning around semantic events, WALL-WM achieves unprecedented generalization across tasks and environments, outperforming traditional models.
Current robot manipulation benchmarks fail to capture the messy reality of real-world deployment, so this work introduces a new benchmark, ManipArena, to close the sim2real gap.
