Sampling methods for motion planning and control using learned spaces

Provider
Czech Science Foundation
Programme
Standard projects
Call for proposals
SGA0202600001
Main participants
České vysoké učení technické v Praze / Fakulta elektrotechnická
Contest type
VS - Public tender
Contract ID
26-22606S

Project name in Czech
Sampling methods for motion planning and control using learned spaces
Annotation in Czech
The project is motivated by a scenario where an assistant robot helps people manipulate household objects, e.g., carrying items to/from a fridge. Sampling-based motion planning and control are challenging in this context, as the robot has many degrees of freedom and operates in constricted spaces (amongst or even in furniture, cupboards, etc.), which leads to the well-known narrow passage problem. We aim to cope with this issue by constructing and exploring a lower-dimensional space (latent space) of the configuration space. We employ an autoencoder neural network to obtain the space and design new methods for its exploration. Searching in this space will enable faster and more efficient planning, particularly in constricted environments. We will also investigate the detection of homotopy classes to improve planning diversity and robustness. For control, we focus on developing new stochastic controllers based on Model Predictive Path Integral (MPPI).

R&D category
ZV - Basic research
OECD FORD - main branch
20204 - Robotics and automatic control
OECD FORD - secondary branch
—
OECD FORD - another secondary branch
—
CEP - equivalent branches <br>(according to the <a href="http://www.vyzkum.cz/storage/att/E6EF7938F0E854BAE520AC119FB22E8D/Prevodnik_oboru_Frascati.pdf">converter</a>)
JD - Use of computers, robotics and its application

Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data delivery code
CEP26-GA0-GA-R
Data delivery date
May 6, 2026

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