Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems
By João Silvério, Leonel Dario Rozo, Sylvain Calinon, Darwin G. Caldwell. 2015
83 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yuke Zhu
- Li Fei-Fei
- Silvio Savarese
- Sonia Chernova
- Ajay Mandlekar
- Harish Chaandar Ravichandar
- Sergey Levine
- Roberto Mart'in-Mart'in
In Control and Systems Engineering
- Sonia Chernova
- Yuke Zhu
- Li Fei-Fei
- Ajay Mandlekar
- Harish Chaandar Ravichandar
- Silvio Savarese
- Aude Gemma Billard
- Sylvain Calinon
Citations in the corpus
- Learning Multi-Arm Manipulation Through Collaborative Teleoperation (2020, background): “Some prior works [13, 21, 33, 43] have leveraged the paradigm of programming by demonstration, but these approaches often requires large modeling assumptions, and may not generalize well to different environment configurations.”
- Learning position and orientation dynamics from demonstrations via contraction analysis (2018, methodology): “The algorithm presented in Silvério et al. (2015) learns the pose dynamics involved in bi-manual manipulation tasks in a task-parametrized manner.”
- Learning Physical Collaborative Robot Behaviors From Human Demonstrations (2016, background): “However, the approach also supports tasks with variable end-effector orientations [49].”
- Recent Advances in Robot Learning from Demonstration (2020, background): “Several approaches to trajectory learning, such as dynamical systems (70, 82, 96, 97, 99, 100) and probabilistic inference (13, 62, 81), have been studied in the literature.”