Inverse reinforcement learning control for trajectory tracking of a multirotor UAV
Sign Up to like & getrecommendations! Published in 2017 at "International Journal of Control, Automation and Systems"
DOI: 10.1007/s12555-015-0483-3
Abstract: The main purpose of this paper is to learn the control performance of an expert by imitating the demonstrations of a multirotor UAV (unmanned aerial vehicle) operated by an expert pilot. First, we collect a… read more here.
Keywords: trajectory; inverse reinforcement; control; reinforcement learning ... See more keywords
An Ensemble Fuzzy Approach for Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Fuzzy Systems"
DOI: 10.1007/s40815-018-0535-y
Abstract: In reinforcement learning, a reward function is a priori specified mapping that informs the learning agent how well its current actions and states are performing. From the viewpoint of training, reinforcement learning requires no labeled… read more here.
Keywords: reinforcement learning; method; reward function; inverse reinforcement ... See more keywords
Map matching on low sampling rate trajectories through deep inverse reinforcement learning and multi-intention modeling
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Geographical Information Science"
DOI: 10.1080/13658816.2024.2391411
Abstract: Abstract Analyzing freight vehicle movements using GPS trajectory data presents challenges due to environmental conditions and hardware limitations impacting data accuracy. Map matching, the process of aligning GPS signals with road networks, facilitates accurate route… read more here.
Keywords: low sampling; inverse reinforcement; reinforcement learning; map matching ... See more keywords
Deep Inverse Reinforcement Learning for Structural Evolution of Small Molecules
Sign Up to like & getrecommendations! Published in 2020 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbaa364
Abstract: The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the… read more here.
Keywords: learning structural; inverse reinforcement; deep inverse; reward function ... See more keywords
Online Personalization of Compression in Hearing Aids via Maximum Likelihood Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3178594
Abstract: A key function of modern hearing aids is compression or mapping of sound to the residual hearing range of those suffering from hearing loss. This paper presents a machine learning approach to personalize compression in… read more here.
Keywords: inverse reinforcement; compression hearing; maximum likelihood; compression ... See more keywords
Bounded Low Latency via Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2024.3411073
Abstract: Accurate traffic prediction is essential for effective resource utilization and improving user experience quality in next generation wireless networks. Machine Learning (ML) techniques offer promising results for traditional scenarios; however, pre-sampling network traffic for training… read more here.
Keywords: inverse reinforcement; network traffic; prediction; traffic prediction ... See more keywords
Inferring Human-Robot Performance Objectives During Locomotion Using Inverse Reinforcement Learning and Inverse Optimal Control
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3143579
Abstract: Quantitatively characterizing a locomotion performance objective for a human-robot system is an important consideration in the assistive wearable robot design towards human-robot symbiosis. This problem, however, has only been addressed sparsely in the literature. In… read more here.
Keywords: inverse reinforcement; control; human robot; robot ... See more keywords
Spatiotemporal Costmap Inference for MPC Via Deep Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3146635
Abstract: It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from… read more here.
Keywords: costmap inference; reinforcement learning; inference mpc; inverse reinforcement ... See more keywords
Android as a Receptionist in a Shopping Mall Using Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3180042
Abstract: For human-robot interaction (HRI), it is difficult to hand-craft all the rules for robots owing to diverse situations. Therefore, inverse reinforcement learning (IRL) is a potential solution that helps transfer human knowledge about interactions to… read more here.
Keywords: inverse reinforcement; shopping mall; reinforcement learning; hri ... See more keywords
Energy-Based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3188100
Abstract: This work reports ondeveloping a deep inverse reinforcement learning method for legged robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive sensory data. Existing works use robot-agnostic exteroceptive environmental features or handcrafted kinematic features;… read more here.
Keywords: deep inverse; inverse reinforcement; energy; reinforcement learning ... See more keywords
Inverse Reinforcement Learning for Trajectory Imitation Using Static Output Feedback Control.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2023.3241015
Abstract: This article studies the trajectory imitation control problem of linear systems suffering external disturbances and develops a data-driven static output feedback (OPFB) control-based inverse reinforcement learning (RL) approach. An Expert-Learner structure is considered where the… read more here.
Keywords: static output; inverse reinforcement; control; output ... See more keywords