Diffusion models for in-context meta-learning of robot dynamics outperform deterministic Transformers in robustness to distribution shifts while enabling real-time operation via warm-started sampling.
A review of learning-based dynamics models for robotic manipulation
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A learning framework that predicts pick-and-place affordances for hitch knots from unordered keypoints and images via graph and convolutional autoencoders fused by cross-attention.
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Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
Diffusion models for in-context meta-learning of robot dynamics outperform deterministic Transformers in robustness to distribution shifts while enabling real-time operation via warm-started sampling.
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RoboHitch: Learning Visual Affordance from Disordered Keypoints for Hitch Knots Tying
A learning framework that predicts pick-and-place affordances for hitch knots from unordered keypoints and images via graph and convolutional autoencoders fused by cross-attention.
- Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video