Paper Reading AI Learner

Self-Supervised Antipodal Grasp Learning with Fine-Grained Grasp Quality Feedback in Clutter

2022-05-11 14:02:03
Yanxu Hou, Jun Li, I-Ming Chen

Abstract

It is a challenging goal in robotics to make a robot grasp like a human being in a cluttered environment. Self-supervised grasp learning is one of the most promising approaches to human-like robotic grasp. However, due to inadequate feedback on grasp quality, almost the existing self-supervised grasp learning methods are coarse-grained. This paper proposes a fine-grained antipodal grasp learning (FAGL) method with augmented learning feedback. First, an indicator called antipodal degree of a grasp (ADG) is defined by a non-increasing monotonous function. ADG reflecting fine-grained grasp quality is evaluated indirectly by the destructive effect of a grasp on the environment via scene images. Next, we design a restorative sampling strategy to collect the samples of fine-grained antipodal grasps and propose a refined affordance network to generate grasp affordance maps for FAGL to decide grasp policies. Finally, in grasping actual metal workpieces, FAGL outperforms its peers in terms of grasp success rate and ADG in cluttered and adversarial scenarios by reducing the grasp effects on the surroundings. The results of extensive experiments show that our method has great potential for industrial application.

Abstract (translated)

URL

https://arxiv.org/abs/2205.05508

PDF

https://arxiv.org/pdf/2205.05508.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot