Results 111 to 120 of about 139,994 (273)

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

CAAI Transactions on Intelligence Technology
Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence ...
Zaipeng Xie, Cheng Ji, Chentai Qiao, WenZhan Song, Zewen Li, Yufeng Zhang, Yujing Zhang   +6 more
doaj   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

A Review on Sensor Technologies, Control Approaches, and Emerging Challenges in Soft Robotics

Advanced Robotics Research, EarlyView.
This review provides an introspective of sensors and controllers in soft robotics. Initially describing the current sensing methods, then moving on to the control methods utilized, and finally ending with challenges and future directions in soft robotics focusing on the material innovations, sensor fusion, and embedded intelligence for sensors and ...
Ean Lovett, Maxwell Hammond, Niloufar Seyfi, Amirreza Fahim Golestaneh, Venanzio Cichella, Caterina Lamuta   +5 more
wiley   +1 more source

Durability of Soft Pneumatic Actuators: A Review and Benchmarking Protocol

Advanced Robotics Research, EarlyView.
Lack of durability is a key challenge hindering the broad scale adoption of soft pneumatic actuators (SPAs) in automation industries. This review provides a comprehensive overview of existing research on SPA durability, introduces a standardized durability benchmarking protocol to consolidate the testing of SPAs, and outlines promising directions for ...
Dickson Chiu Yu Wong, Yucheng Luo, Rob Bernardus Nicolaas Scharff   +2 more
wiley   +1 more source

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

A Deep Reinforcement Learning-Based Cooperative Guidance Strategy Under Uncontrollable Velocity Conditions

Aerospace
We present a novel approach to generating a cooperative guidance strategy using deep reinforcement learning to address the challenge of cooperative multi-missile strikes under uncontrollable velocity conditions.
Hao Cui, Ke Zhang, Minghu Tan, Jingyu Wang   +3 more
doaj   +1 more source

Hybrid Continuum Robot Designs and Architectures for Healthcare Applications

Advanced Robotics Research, EarlyView.
Hybrid continuum robots represent an emerging class of flexible manipulators that blend materials, structures, and actuation concepts from the established fields of soft and continuum robotics. This review introduces an accessible framework to distinguish key hybridization approaches, surveys current designs aimed at complex clinical applications, and ...
Burak Ozdemir, Muhammad A. Khalid, Manish Chauhan, Pietro Valdastri, James H. Chandler   +4 more
wiley   +1 more source

Multi‐Agent Reinforcement Learning for Cyber Defence Transferability and Scalability

Applied AI Letters
Reinforcement learning (RL) has shown to be effective for simple automated cyber defence (ACD) type tasks. However, there are limitations to these approaches that prevent them from being deployed onto real‐world hardware.
Andrew Thomas, Matthew Yates, Oliver Osborne   +2 more
doaj   +1 more source

Learning graph based individual intrinsic reward for multi-agent reinforcement learning

ICT Express
Designing a reward function is a critical challenge in reinforcement learning. However, as environments become more complex and tasks grow more difficult, designing a reward function that drives optimal behavior becomes increasingly challenging.
Seokhun Ju, Seungyub Han, Taehyun Cho, Jungwoo Lee, Taeyoung Lee, Minkyoung Kim, Jinho An   +6 more
doaj   +1 more source

Concept Learning for Cooperative Multi-Agent Reinforcement Learning

2025 IEEE 26th China Conference on System Simulation Technology and its Applications (CCSSTA)
Despite substantial progress in applying neural networks (NN) to multi-agent reinforcement learning (MARL) areas, they still largely suffer from a lack of transparency and interoperability. However, its implicit cooperative mechanism is not yet fully understood due to black-box networks.
Zhonghan Ge, Yuanyang Zhu, Chunlin Chen
openaire   +2 more sources