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Technical University of Munich (TUM)
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Sparse demonstrations can now effectively bootstrap humanoid loco-manipulation learning, reducing the need for constant human oversight.
By systematically guiding diffusion policies to explore underrepresented behaviors, this framework uncovers novel trajectories that traditional methods often overlook.
Automated discovery of complex humanoid manipulation skills could revolutionize how robots learn and adapt to new tasks without human input.
