Multiple view geometry in computer vision
Multiple view geometry in computer vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Pose Recovery Using Learned Hierarchical Object Models
Cognitive Vision
Learning object-specific grasp affordance densities
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Grasp planning from human prehension
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Reactive grasping using optical proximity sensors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
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Grasping is one of the most important abilities needed for future service robots. In the task of picking up an object from between clutter, traditional robotics approaches would determine a suitable grasping point and then use a movement planner to reach the goal. The planner would require precise and accurate information about the environment and long computation times, both of which are often not available. Therefore, methods are needed that execute grasps robustly even with imprecise information gathered only from standard stereo vision. We propose techniques that reactively modify the robot's learned motor primitives based on non-parametric potential fields centered on the Early Cognitive Vision descriptors. These allow both obstacle avoidance, and the adapting of finger motions to the object's local geometry. The methods were tested on a real robot, where they led to improved adaptability and quality of grasping actions.