Brayan S. Zapata-Impata
Papers
9
Total Citations
243
H-Index
7
About
Brayan S. Zapata-Impata is a leading researcher in robotic manipulation, specializing in tactile perception, grasp stability, and vision-driven grasping systems. His work addresses fundamental challenges in autonomous robotics, particularly the detection of slip and grasp stability during manipulation. His most influential paper, "Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection" (91 citations), introduces a novel deep learning approach to classify translational and rotational slippage, significantly advancing beyond simple stability detection. He also developed fast geometry-based methods for computing grasping points on 3D point clouds (58 citations), enabling robots to autonomously grasp unknown objects. His research on tactile-driven grasp stability and slip prediction (26 citations) further integrates tactile feedback into robotic systems. Notably, he has explored generating tactile data from 3D vision (14 citations) to reduce reliance on physical contact, and pioneered vision-driven collaborative grasping systems tele-operated by surface electromyography (9 citations), merging computer vision with human bio-signals for intuitive robot control. With over 240 total citations, Zapata-Impata's contributions are pivotal for advancing robust, sensor-rich robotic grasping in industrial and service applications.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Using Geometry to Detect Grasping Points on 3D Unknown Point Cloud30 citations · 2017
- 4Tactile-Driven Grasp Stability and Slip Prediction26 citations · 2019
- 5Generation of Tactile Data From 3D Vision and Target Robotic Grasps14 citations · 2020
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- 9Prediction of Tactile Perception from Vision on Deformable Objects2 citations · 2020