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Found 11 papers, 7 papers with code
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method.
DLOFTBs -- Fast Tracking of Deformable Linear Objects with B-splines
While manipulating rigid objects is an extensively explored research topic, deformable linear object (DLO) manipulation seems significantly underdeveloped.
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction
This paper demonstrates how an efficient representation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up both the inference and training of a DNN-based motion planner.
Tuning of extended state observer with neural network-based control performance assessment
The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics.
A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups
We propose a computationally efficient $G$-invariant neural network that approximates functions invariant to the action of a given permutation subgroup $G \leq S_n$ of the symmetric group on input data.
Learning from Experience for Rapid Generation of Local Car Maneuvers
Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy.
Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter
The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise.
A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment
An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic.
Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks
A crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which is considered as a significant challenge.
A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups
The key element of the proposed network architecture is a new $G$-invariant transformation module, which produces a $G$-invariant latent representation of the input data.
