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Policies may succeed in tasks but still violate deformation tolerances, revealing a critical gap in current evaluation methods for deformable-object manipulation.
LLM-driven self-evolving architectures can outperform expert-designed models in robotic tactile perception tasks, achieving unprecedented levels of performance and diversity.
Forget rigid-body contact and stable grasping: biological micromanipulation demands a new "micro-dexterity" framework to rethink manipulation primitives at tiny scales.
Synthesizing realistic microscopy images of microrobots, complete with depth-dependent optical effects, is now possible even with limited training data, thanks to a frequency-aware diffusion model.
