A Hierarchical Compliance-Based Contextual Policy Search for Robotic Manipulation Tasks With Multiple Objectives
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3192435
Abstract: Contextual policy search methods have demonstrated the potential to acquire robotic skill generalization on trajectory-shaping-based tasks. However, it is still challenging for robotic contact-rich manipulation tasks because contact force regulation, reference trajectory adaptation, and task… read more here.
Keywords: policy search; contextual policy; policy; manipulation tasks ... See more keywords
Numerical Quadrature for Probabilistic Policy Search
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2018.2879335
Abstract: Learning control policies has become an appealing alternative to the derivation of control laws based on classic control theory. Model-based approaches have proven an outstanding data efficiency, especially when combined with probabilistic models to eliminate… read more here.
Keywords: probabilistic policy; quadrature probabilistic; policy; policy search ... See more keywords
Dual REPS: A Generalization of Relative Entropy Policy Search Exploiting Bad Experiences
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Robotics"
DOI: 10.1109/tro.2017.2679202
Abstract: Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only.… read more here.
Keywords: policy; search; generalization relative; policy search ... See more keywords
Guided Policy Search for Sequential Multitask Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2018.2800040
Abstract: Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as… read more here.
Keywords: sequential multitask; multitask learning; policy search;
Nonconvex Policy Search Using Variational Inequalities
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computation"
DOI: 10.1162/neco_a_01004
Abstract: Policy search is a class of reinforcement learning algorithms for finding optimal policies in control problems with limited feedback. These methods have been shown to be successful in high-dimensional problems such as robotics control. Though… read more here.
Keywords: policy; search using; variational inequalities; policy search ... See more keywords