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Traditional residual corrections can falter under dynamics shifts, but Warp RL reshapes action distributions to achieve superior performance in challenging environments.
Combining learning and geometric optimization, this framework achieves a 60.9% grasp success rate, outperforming traditional methods by a significant margin.
RoboLab exposes critical performance gaps in leading robotic models, revealing that high-fidelity simulations can better assess generalization than traditional benchmarks.
