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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.
