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Robot Grasping in a Heavily Cluttered Environment

Korea Advanced Institute of Science and Technology (KAIST) student Dongwon Son has recently published interesting research about reactive grasping in a heavily cluttered environment in IEEE Robotics and Automation Letters.

grasping in a heavily cluttered environment-pic 1
Reactive robot grasping in a Heavily cluttered environment. Courtesy of Samsung Research and Dongwon Son.

This study proposed a closed-loop framework for predicting the six-degree-of-freedom (dof) grasp in a heavily cluttered environment using vision observations.

robot grasping prediction results
prediction results in robot grasping. Courtesy of Samsung Research and Dongwon Son.

Experimental results on a robot in an environment with a lot of clutter showed that the grasping success rate had improved quantitatively compared to the previous algorithms. Additionally, the framework is able to respond qualitatively to a dynamic change in the environment and clean up the table successfully.

experimental results robot grasping
Experimental results of the reactive robot grasping show an improvement relative to the previous work. Courtesy of Samsung Research and Dongwon Son.

Their method involves formulating the grasping problem as a Hidden Markov Model and applying a particle filter in order to infer grasp. To make the particle filter process possible, they developed a lightweight Convolutional Neural Network (CNN) model for Real-time evaluation and initialization of grasp samples.

particle process in robot grasping
The grasping problem is formulated as a Hidden Markov Model with a particle filter to infer grasp. Courtesy of Samsung Research and Dongwon Son.

References:
https://arxiv.org/abs/2205.13146
https://dongwon-son.github.io/

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