Physics-Based Self-Supervised Grasp Pose Detection
Current industrial robotic manipulators have made their lack of flexibility evident. The systems must know beforehand the piece and its position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, a...
Saved in:
Main Authors: | Jon Ander Ruiz, Ander Iriondo, Elena Lazkano, Ander Ansuategi, Iñaki Maurtua |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-12-01
|
Series: | Machines |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-1702/13/1/12 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations
by: Montassar Aidi Sharif, et al.
Published: (2025-02-01) -
Robot Closed-Loop Grasping Based on Deep Visual Servoing Feature Network
by: Junqi Luo, et al.
Published: (2025-01-01) -
Regeneration of natural grasp prehensions on underactuated robot‐hand through kinaesthetic guidance
by: R. Chattaraj, et al.
Published: (2017-03-01) -
Motor-Less Robotic Gripper: Driving Mechanism by Robotic Manipulator Movement
by: Toshihiro Nishimura, et al.
Published: (2025-01-01) -
Application of Bayesian Optimization in Gripper Design for Effective Grasping
by: Marco Todescato, et al.
Published: (2025-01-01)