Constraints extraction from asymmetrical bimanual tasks and their use in coordinated behavior
By Ana Lucia Pais, Aude Gemma Billard. 2018
41 citations recorded by the source corpus.
Abstract In this paper we focus on extracting a parametrization of asymmetrical bimanual tasks from human demonstration. Two arms coordinate while being in physical contact and fulfilling complementary aspects of the task: one arm is mostly assistive while the other performs active manipulation. Such a task can be executed either autonomously by a bimanual robot, or collaboratively by a single robotic arm performing the assistive or active role in collaboration with a human. We thus decompose the demonstrated task in a set of actions and for each action we extract: the role of the arms as master or slave, the type of coupling as force–motion or motion–motion coupling with the corresponding stiffness modulation, and the transition condition. We discuss how this applies to the three execution cases mentioned above. Additionally for the collaborative case we study hand-related features that allow the robot to anticipate and adapt to the user’s actions. We validate our approach on common daily tasks.
Subfields: Cognitive Neuroscience, 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 Cognitive Neuroscience
- Danica Kragic
- Roberto Mart'in-Mart'in
- Carme Torras
- Adrià Colomé
- Fabio Amadio
- Silvio Savarese
- Li Fei-Fei
- Ajay Mandlekar
In Control and Systems Engineering
- Yuke Zhu
- Ajay Mandlekar
- Li Fei-Fei
- Silvio Savarese
- Roberto Mart'in-Mart'in
- Fabio Amadio
- Danica Kragic
- Josiah Wong
Citations in the corpus
- Learning Multi-Arm Manipulation Through Collaborative Teleoperation (2020, background): “and twisting [7], by encouraging efficient exploration via intrinsic motivation [8], and leveraging movement primitives or constraints from human demonstrations [3, 29, 40].”
- Exploiting Symmetries in Reinforcement Learning of Bimanual Robotic Tasks (2019, methodology): “For example, in [5] a user-demonstrated bimanual task is reproduced characterizing it through a set of constraints, while in [6] joint synergies are extracted through group factor analysis.”
- Trends and challenges in robot manipulation (2019, methodology): “fruits and vegetables) (65) (middle), and manipulation of objects in collaboration with humans (right) present ongoing difficulties.”