scholarly journals Planning realistic interactions for bimanual grasping and manipulation

2016 ◽  
Author(s):  
Ashok M. Sundaram ◽  
Oliver Porges ◽  
Maximo A. Roa
Keyword(s):  
Grasping And Manipulation ◽  
Bimanual Grasping

scholarly journals Transfer and generalization of learned manipulation between unimanual and bimanual tasks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Trevor Lee-Miller ◽  
Marco Santello ◽  
Andrew M. Gordon
Keyword(s):  
Learning Transfer ◽  
Center Of Pressure ◽  
Center Of Mass ◽  
Pressure Point ◽  
Torque Generation ◽  
Before And After ◽  
Object Center ◽  
High Level ◽  
Bimanual Grasping ◽  
Partial Learning

AbstractSuccessful object manipulation, such as preventing object roll, relies on the modulation of forces and centers of pressure (point of application of digits on each grasp surface) prior to lift onset to generate a compensatory torque. Whether or not generalization of learned manipulation can occur after adding or removing effectors is not known. We examined this by recruiting participants to perform lifts in unimanual and bimanual grasps and analyzed results before and after transfer. Our results show partial generalization of learned manipulation occurred when switching from a (1) unimanual to bimanual grasp regardless of object center of mass, and (2) bimanual to unimanual grasp when the center of mass was on the thumb side. Partial generalization was driven by the modulation of effectors’ center of pressure, in the appropriate direction but of insufficient magnitude, while load forces did not contribute to torque generation after transfer. In addition, we show that the combination of effector forces and centers of pressure in the generation of compensatory torque differ between unimanual and bimanual grasping. These findings highlight that (1) high-level representations of learned manipulation enable only partial learning transfer when adding or removing effectors, and (2) such partial generalization is mainly driven by modulation of effectors’ center of pressure.

Analysis of Multi-fingered Grasping and Manipulation

1990 ◽  
pp. 187-208 ◽  
Author(s):  
Tsuneo Yoshikawa ◽  
Kiyoshi Nagai
Keyword(s):  
Grasping And Manipulation

scholarly journals Usability Study of Learning-Based Pose Estimation of Industrial Objects from Synthetic Depth Data

2021 ◽  
pp. 285-296
Author(s):  
Stefan Thalhammer ◽  
Timothy Patten ◽  
Markus Vincze
Keyword(s):  
Pose Estimation ◽  
Scene Understanding ◽  
Important Task ◽  
Usability Study ◽  
Depth Data ◽  
Industrial Setting ◽  
Grasping And Manipulation ◽  
Object Pose Estimation ◽  
Assistance Systems ◽  
Mesh Models

AbstractFor visual assistance systems deployed in an industrial setting, precise object pose estimation is an important task in order to support scene understanding and to enable subsequent grasping and manipulation. Industrial environments are especially challenging since mesh-models are usually available while physical objects are not or are expensive to model. Manufactured objects are often similar in appearance, have limited to no textural cues and exhibit symmetries. Thus, these are especially challenging for recognizers that are meant to provide detection, classification and pose estimation on instance level. A usability study of a recent synthetically trained learning-based recognizer for these particular challenges is conducted. Experiments are performed on the challenging T-LESS dataset due to its relevance for industry.

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