Prolonging Robot Lifespan Using Fatigue Balancing with Reinforcement Learning

Authors: Costa, Francesco, Mozaffari, Saeed, Alirezaee, Reza, Ahmadi, Madjid, Alirezaee, Shahpour

Source: 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR) Mechanical Engineering and Robotics Research (ICMERR), 2022 7th International Conference on. :45-50 Dec, 2022

Relation: 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR)

An effective framework with hybrid augmentation for visual reinforcement learning generalization

Authors: Fang, Yu ^a, Zhang, Xuehe ^{a, ⁎}, Cheng, Haoshu ^b, Zang, Xizhe ^a, Li, Changle ^a, Zhao, Jie ^a

Source: In Neurocomputing 7 December 2025 657

Shaping Reward Signals in Reinforcement Learning Using Constraint Programming

Authors: Chao Yin, Quentin Cappart, Gilles Pesant

Source: Lecture Notes in Computer Science ISBN: 9783031959752

Subject Terms: Reinforcement Learning in Robotics, Computer Networks and Communications, Artificial Intelligence, Constraint Satisfaction and Optimization, Scheduling and Optimization Algorithms, Industrial and Manufacturing Engineering

Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search

Authors: Gao, Xiaojie, Jin, Yueming, Dou, Qi, Heng, Pheng-Ann

Source: 2020 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2020 IEEE International Conference on. :8440-8446 May, 2020

Relation: 2020 IEEE International Conference on Robotics and Automation (ICRA)

Risk-Sensitive Autonomous Exploration of Unknown Environments: A Deep Reinforcement Learning Perspective

Authors: Sarfi, Mohammad Hossein, Bisheban, Mahdis^{Aff1, IDs10846025022352_cor1}

Source: Journal of Intelligent & Robotic Systems: with a special section on Unmanned Systems. 111(1)

HiER: Highlight Experience Replay for Boosting Off-Policy Reinforcement Learning Agents

Authors: Dániel Horváth, Jesús Bujalance Martín, Ferenc Gàbor Erdos, Zoltán Istenes, Fabien Moutarde

Contributors: Centre de Robotique (CAOR), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL), Centre of Excellence in Production Informatics and Control, Institute for Computer Science and Control, Hungarian Research Network, Budapest, Eötvös Loránd University (ELTE)

Source: IEEE Access, Vol 12, Pp 100102-100119 (2024)

Subject Terms: robotics, FOS: Computer and information sciences, reinforcement learning, Computer Science - Robotics, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], experience replay, QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány, Electrical engineering. Electronics. Nuclear engineering, Curriculum learning, Curriculum learning experience replay reinforcement learning and robotics, and robotics, Robotics (cs.RO), TK1-9971

File Description: text

Access URL: http://arxiv.org/abs/2312.09394
https://doaj.org/article/2aa566d34503416a996ac6f4741fa09d
https://eprints.sztaki.hu/10775/

Reachability Graph Learning with Reinforcement Learning Control for Robotic Tasks

Authors: Bonnavaud, Hedwin, Albore, Alexandre, Rachelson, Emmanuel

Contributors: Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), DTIS, ONERA, Université de Toulouse Toulouse, ONERA-Communauté d'universités et établissements de Toulouse (Comue de Toulouse), ANR-21-ASRO-0004,PARHéRo,Méthodes hybrides de planification et apprentissage pour la robotique hétérogène robuste(2021)

Source: 2025 10th International Conference on Control and Robotics Engineering (ICCRE)
https://hal.science/hal-05285544
2025 10th International Conference on Control and Robotics Engineering (ICCRE), May 2025, Nagoya, France. pp.105-110, ⟨10.1109/ICCRE65455.2025.11093311⟩
https://www.iccre.org/iccre2025.html

Subject Terms: Reinforcement learning, Reinforcement learning planning Robotics, navigation and control, gnc - guidance, machine learning guidage, Robotics, planning, navigation et contrôle, apprentissage automatique, robotique, [PHYS]Physics [physics], [MATH]Mathematics [math], [SPI]Engineering Sciences [physics]

Subject Geographic: Nagoya, France

Availability: https://hal.science/hal-05285544
https://hal.science/hal-05285544v1/document
https://hal.science/hal-05285544v1/file/DTIS2025-048-rgl_robots-Accept%C3%A9e.pdf
https://doi.org/10.1109/ICCRE65455.2025.11093311

Autonomous Exploration and Mapping of Unknown Environments

Authors: Sarfi, Mohammad Hossein

Contributors: Bisheban, Mahdis, Thekinen, Joseph Davis, Dankers, Arne, PRISM

Subject Terms: Deep Reinforcement Learning in robotics, Engineering--Mechanical, Autonomous exploration, Mapless navigation, Risk-sensitive decision making, Multi-robot coordination

File Description: application/pdf

Robust bipedal locomotion through an MPC-based residual learning framework

Authors: Arribalzaga Jové, Carlos

Contributors: Torras, Carme, Sentís Álvarez, Luis, Institut de Robòtica i Informàtica Industrial, University of Texas at Austin

Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Subject Terms: reinforcement learning, Robòtica, Àrees temàtiques de la UPC::Matemàtiques i estadística, model predictive control, reduced-order model, Àrees temàtiques de la UPC::Física, whole-body control, humanoid, Robotics, deep reinforcement learning in robotics, angular momentum, robust policy, bipedal locomotion, walking, hierarchical framework, Classificació AMS::68 Computer science::68T Artificial intelligence, task space learning, Reinforcement learning, Aprenentatge per reforç, Classificació AMS::70 Mechanics of particles and systems::70Q05 Control of mechanical systems, Markov decision process, inverted pendulum, residual learning

File Description: application/pdf

Access URL: https://hdl.handle.net/2117/411414

Safe Deep Reinforcement Learning: Enhancing the Reliability of Intelligent Systems

Authors: Davide Corsi

Subject Terms: Reinforcement Learning, Safety, Robotics, Verification, Neural Networks

File Description: application/pdf

Access URL: https://hdl.handle.net/11562/1093466

Data-Efficient Robot Learning using Priors from Simulators ; Apprentissage efficace en données pour la robotique à l'aide de simulateurs

Authors: Kaushik, Rituraj

Contributors: Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Centre Inria de l'Université de Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université de Lorraine, Jean-Baptiste Mouret

Source: https://hal.univ-lorraine.fr/tel-02976390 ; Artificial Intelligence [cs.AI]. Université de Lorraine, 2020. English. ⟨NNT : 2020LORR0105⟩.

Subject Terms: Reinforcement learning in robotics, Sim to real in robotics, Adaptive robots, Data-efficient robot learning, Passage de la simulation à la réalité, Robots adaptatifs, Apprentissage par renforcement en robotique, Apprentissage efficace en données pour la robotique, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SPI.AUTO]Engineering Sciences [physics]/Automatic, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]

Relation: NNT: 2020LORR0105

Availability: https://hal.univ-lorraine.fr/tel-02976390
https://hal.univ-lorraine.fr/tel-02976390v1/document
https://hal.univ-lorraine.fr/tel-02976390v1/file/DDOC_T_2020_0105_KAUSHIK.pdf

Data-Efficient Robot Learning using Priors from Simulators

Authors: Kaushik, Rituraj

Contributors: UL, Thèses

Subject Terms: Sim to real in robotics, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Reinforcement learning in robotics, Passage de la simulation à la réalité, Robots adaptatifs, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Data-efficient robot learning, Apprentissage efficace en données pour la robotique, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Adaptive robots, Apprentissage par renforcement en robotique, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]

File Description: application/pdf

Access URL: https://hal.univ-lorraine.fr/tel-02976390v1

Efficient supervision for robot learning via imitation, simulation, and adaptation

Authors: Wulfmeier, Markus

Committee Members: Zisserman, Andrew; Riedmiller, Martin; Newman, Paul; Posner, Ingmar

URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819

Robust bipedal locomotion through an MPC-based residual learning framework

Authors: Institut de Robòtica i Informàtica Industrial, University of Texas at Austin, Torras, Carme, Sentís Álvarez, Luis, Arribalzaga Jové, Carlos

Index Terms: Àrees temàtiques de la UPC::Matemàtiques i estadística, Àrees temàtiques de la UPC::Física, Robotics, Reinforcement learning, humanoid, bipedal locomotion, walking, hierarchical framework, reinforcement learning, task space learning, model predictive control, reduced-order model, inverted pendulum, residual learning, Markov decision process, robust policy, whole-body control, deep reinforcement learning in robotics, angular momentum, Robòtica, Aprenentatge per reforç, Classificació AMS::68 Computer science::68T Artificial intelligence, Classificació AMS::70 Mechanics of particles and systems::70Q05 Control of mechanical systems, Bachelor thesis

URL: https://hdl.handle.net/2117/411414