Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the problem of grasp pose detection in point clouds.  follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network.  propose two new representations of grasp candidates, and  quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as ll as pretraining and prior knowledge significantly improve grasp detection.  evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work.
['Marcus Gualtieri', 'Andreas ten Pas', 'Kate Saenko', 'Robert Platt']
I am writing a research paper on a idea High precision grasp pose detection in dense clutter
[{"from": "human", "value": "I am writing a research paper on a idea High precision grasp pose detection in dense clutter"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the problem of grasp pose detection in point clouds.  follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network.  propose two new representations of grasp candidates, and  quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as ll as pretraining and prior knowledge significantly improve grasp detection.  evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work."}]