ABCD and VONs enable visually and mechanically interpretable latent dynamics learning for soft robots from video, with reported multi-step prediction gains of 5.8x and 3.5x on two-segment cases.
A review of learning-based dynamics models for robotic manipulation,
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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.
citing papers explorer
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Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
ABCD and VONs enable visually and mechanically interpretable latent dynamics learning for soft robots from video, with reported multi-step prediction gains of 5.8x and 3.5x on two-segment cases.
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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.