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A custom-designed tendon-driven wrist, combined with a particle-spring model, enables precise and robust control of highly flexible objects like spinning handkerchiefs.
VLAs can learn to adapt to new environments at test time without any fine-tuning, achieving significant performance gains on robotic manipulation and Atari games.
Musculoskeletal robots can achieve significantly better locomotion by co-designing morphology and control, simultaneously optimizing muscle strength, velocity, and stiffness, rather than relying on fixed physiological parameters.
By explicitly disentangling degradation and semantic features with wavelet attention, CWP-Net achieves superior all-in-one image restoration, outperforming previous methods hampered by spurious correlations and biased degradation estimation.
By using optimal transport to guide cross-attention, SceneTransporter disentangles image patches and 3D latents, leading to more coherent and geometrically faithful 3D scene generation from single images.
Ditch density estimation in maximum entropy RL: FLAC leverages kinetic energy regularization within a Schr枚dinger Bridge framework to achieve state-of-the-art performance without ever needing action log-densities.
