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Interactive imitation learning for dexterous robotic manipulation: challenges and perspectives—a survey [PDF]

Frontiers in Robotics and AI
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with
Edgar Welte, Rania Rayyes
doaj   +2 more sources

Leveraging imitation learning in agricultural robotics: a comprehensive survey and comparative analysis [PDF]

Frontiers in Robotics and AI
Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control ...
Siavash Mahmoudi, Amirreza Davar, Amirreza Davar, Pouya Sohrabipour, Ramesh Bahadur Bist, Yang Tao, Dongyi Wang, Dongyi Wang, Dongyi Wang   +8 more
doaj   +2 more sources

Visual imitation learning from one-shot demonstration for multi-step robot pick and place tasks [PDF]

Scientific Reports
Imitation learning provides an intuitive approach for robot programming by enabling robots to learn directly from human demonstrations. While recent visual imitation learning methods have shown promise, they often depend on large datasets, which limits ...
Shuang Lu, Christian Härdtlein, Johaness Schilp   +2 more
doaj   +2 more sources

Anti-Jamming Communication Using Imitation Learning [PDF]

Entropy, 2023
The communication reliability of wireless communication systems is threatened by malicious jammers. Aiming at the problem of reliable communication under malicious jamming, a large number of schemes have been proposed to mitigate the effects of malicious
Zhanyang Zhou, Yingtao Niu, Boyu Wan, Wenhao Zhou   +3 more
doaj   +2 more sources

Domain Adaptation for Imitation Learning Using Generative Adversarial Network [PDF]

Sensors, 2021
Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert.
Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan, Eiji Kamioka   +3 more
doaj   +2 more sources

Neuroprosthetic Decoder Training as Imitation Learning. [PDF]

PLoS Comput Biol, 2016
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task.
Merel J, Carlson D, Paninski L, Cunningham JP.   +3 more
europepmc   +6 more sources

Imitation Learning [PDF]

ACM Computing Surveys, 2017
Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing ...
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, Chrisina Jayne   +3 more
openaire   +3 more sources

Co-imitation: Learning Design and Behaviour by Imitation

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
The co-adaptation of robots has been a long-standing research endeavour with the goal of adapting both body and behaviour of a robot for a given task, inspired by the natural evolution of animals. Co-adaptation has the potential to eliminate costly manual hardware engineering as well as improve the performance of systems.
Rajani, Chang, Arndt, Karol, Blanco Mulero, David, Luck, Kevin, Kyrki, Ville   +4 more
openaire   +5 more sources

imitation: Clean Imitation Learning Implementations

, 2022
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code.
Gleave, Adam, Taufeeque, Mohammad, Rocamonde, Juan, Jenner, Erik, Wang, Steven H., Toyer, Sam, Ernestus, Maximilian, Belrose, Nora, Emmons, Scott, Russell, Stuart   +9 more
openaire   +2 more sources

Divide & Conquer Imitation Learning

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
When cast into the Deep Reinforcement Learning framework, many robotics tasks require solving a long horizon and sparse reward problem, where learning algorithms struggle. In such context, Imitation Learning (IL) can be a powerful approach to bootstrap the learning process.
Chenu, Alexandre, Perrin-Gilbert, Nicolas, Sigaud, Olivier   +2 more
openaire   +3 more sources