Paper Reading AI Learner

LMI-based Variable Impedance Controller design from User Demonstrations and Preferences

2022-09-21 10:19:01
Alberto San-Miguel, Guillem Alenyà, Vicenç Puig

Abstract

In this paper, we introduce a new off-line method to find suitable parameters of a Variable Impedance Control using the Learning from Demonstrations (LfD) paradigm, fulfilling stability and performance constraints while taking into account user's intuition over the task. Considering a compliance profile obtained from human demonstrations, a Linear Parameter Varying (LPV) description of the VIC is given, which allows to state the design problem including stability and performance constraints as Linear Matrix Inequalities (LMIs). Therefore, using a solution-search method, we find the optimal solutions in terms of performance according to user preferences on the desired behaviour for the task. The design problem is validated by comparing the execution from the obtained controller against solutions from designs with relaxed conditions for different user preference sets in a 2-D trajectory tracking task. A pulley looping task is presented as a case study to evaluate the performance of the variable impedance controller against a constant one and its agility and leanness on two one-off modifications of the setup using the user preference mechanism. All the experiments have been performed using a 7-DoF KINOVA Gen3 manipulator.

Abstract (translated)

URL

https://arxiv.org/abs/2209.10244

PDF

https://arxiv.org/pdf/2209.10244.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