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Coding agents are failing to demonstrate robust engineering skills in robotics, with RLE-Bench revealing significant gaps in multimodal reasoning and artifact integration beyond simple control.
Achieving zero-shot transfer of a single diffusion policy for multi-task block pushing could revolutionize robotic manipulation across varied real-world environments.
RepMT-SAC achieves up to 30% better performance in multi-task reinforcement learning by leveraging a task-agnostic core for efficient skill transfer.
