activity
20202023
most citedSpeeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

4 citations · 7 across the 6 of their papers we have counts for

collaborators

8 papers

cs.RO2023★ 1 cited

Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks

Piotr Kicki, Puze Liu, Davide Tateo +4

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…

cs.RO2022

Learning an Efficient Terrain Representation for Haptic Localization of a Legged Robot

Damian Sójka, Michał R. Nowicki, Piotr Skrzypczyński

Although haptic sensing has recently been used for legged robot localization in extreme environments where a camera or LiDAR might fail, the problem of efficiently representing the…

cs.RO2022★ 4 cited

Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

Piotr Kicki, Piotr Skrzypczyński

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 bia…

cs.RO2021

On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM

Jan Wietrzykowski, Piotr Skrzypczyński

We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture…

cs.LG2020★ 1 cited

A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups

Piotr Kicki, Mete Ozay, Piotr Skrzypczyński

We propose a computationally efficient -invariant neural network that approximates functions invariant to the action of a given permutation subgroup of the symmetri…

cs.RO2020

Learning from Experience for Rapid Generation of Local Car Maneuvers

Piotr Kicki, Tomasz Gawron, Krzysztof Ćwian +2

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep…