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ActionFlow: Efficient, Accurate, and Fast Policies with Spatially Symmetric Flow Matching
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Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation
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Semi-Supervised Neural Processes for Articulated Object Interactions
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Learning Latent Lagrangian Dynamics via Riemannian Model Order Reduction
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Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
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Physics Informed Gaussian Processes for Safe Envelope Expansion
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Refined Motion Compensation with Soft Laser Manipulators using Data-Driven Surrogate Models
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Learning Robot Control: From Reinforcement Learning to Differentiable Simulation
Yunlong Song, Davide Scaramuzza
MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints
Maria Krinner*, Angel Romero*, Leonard Bauersfeld, Melanie Zeilinger, Andrea Carron, Davide Scaramuzza
Learning Hierarchical Abstractions of Complex Dynamics using Active Predictive Coding
Vishwas Sathish, Rajesh P. N. Rao
Safe and stable motion primitives via imitation learning and geometric fabrics
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Optimal Soft Robot control using Adiabatic Spectral Submanifolds
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Learning dynamics models for velocity estimation in autonomous racing
Jan Węgrzynowski, Grzegorz Czechmanowski, Piotr Kicki, Krzysztof Walas
D2-RL: Dominant Dynamics for Efficient Model-Based Reinforcement Learning
Shitong Pang, John Alora, Luis Pabon, Marco Pavone
MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations
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Generalizing Spectral Submanifolds to Control-Affine Dynamical Systems
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