Reference-augmented learning with RNN surrogate and stochastic perturbations cuts average position error by 50.9% for 6-DOF tracking on a three-section TDCR compared to non-augmented baselines.
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Closed-form relations and higher-order approximations for the first and second derivatives of the tangent operator on SE(3) are provided in compact matrix form.
LEGO learns a geometry-preserving latent space of humanoid designs from existing examples and performs gradient-free optimization in that space with task losses taken from human motion data.
A robot generalizes one demonstration of placing a single brick to build walls of arbitrary length and layout by approximating motions as screw sequences and using ScLERP/RMRC for planning.
A bidirectional multi-channel GRU dynamics model with residual prediction supports end-to-end neural control for tendon-driven continuum robots, delivering accurate tracking and robustness to unseen payloads without self-excited oscillations.
citing papers explorer
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Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
Reference-augmented learning with RNN surrogate and stochastic perturbations cuts average position error by 50.9% for 6-DOF tracking on a three-section TDCR compared to non-augmented baselines.
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Closed Form Relations and Higher-Order Approximations of First and Second Derivatives of the Tangent Operator on SE(3)
Closed-form relations and higher-order approximations for the first and second derivatives of the tangent operator on SE(3) are provided in compact matrix form.
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LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
LEGO learns a geometry-preserving latent space of humanoid designs from existing examples and performs gradient-free optimization in that space with task losses taken from human motion data.
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Manipulation Planning for Construction Activities with Repetitive Tasks
A robot generalizes one demonstration of placing a single brick to build walls of arbitrary length and layout by approximating motions as screw sequences and using ScLERP/RMRC for planning.
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Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
A bidirectional multi-channel GRU dynamics model with residual prediction supports end-to-end neural control for tendon-driven continuum robots, delivering accurate tracking and robustness to unseen payloads without self-excited oscillations.