Cargando…
Geometry-aware manipulability learning, tracking, and transfer
Body posture influences human and robot performance in manipulation tasks, as appropriate poses facilitate motion or the exertion of force along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control, and design the robot dexterity as a function of...
Autores principales: | Jaquier, Noémie, Rozo, Leonel, Caldwell, Darwin G, Calinon, Sylvain |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8072844/ https://www.ncbi.nlm.nih.gov/pubmed/33994629 http://dx.doi.org/10.1177/0278364920946815 |
Ejemplares similares
-
Sim-to-real via latent prediction: Transferring visual non-prehensile manipulation policies
por: Rizzardo, Carlo, et al.
Publicado: (2023) -
Geometry-Aware Cell Detection with Deep Learning
por: Jiang, Hao, et al.
Publicado: (2020) -
GPU Pro 360 guide to geometry manipulation
por: Engel, Wolfgang
Publicado: (2018) -
Bilateral teleoperation with object-adaptive mapping
por: Gao, Xiao, et al.
Publicado: (2021) -
Learning Transferable Push Manipulation Skills in Novel Contexts
por: Howard, Rhys, et al.
Publicado: (2021)