Results 231 to 240 of about 139,994 (273)

A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo‐Rigid Body Modeling

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
In this research, a paradigm of parameter estimation method for pneumatic soft hand control is proposed. The method includes the following: 1) sampling harmonic damping waves, 2) applying pseudo‐rigid body modeling and the logarithmic decrement method, and 3) deriving position and force control.
Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande   +4 more
wiley   +1 more source

A Multimodal Laser‐Induced Graphene‐Based Flexible Sensor for Soft Robotic Hand Environmental Perception

Advanced Intelligent Systems, EarlyView.
A multimodal laser‐induced graphene (LIG)‐based flexible sensor is developed to detect proximity and contact signals. Integrated into a soft robotic hand, it enables vision‐free object searching and grasping. Combined with a convolutional neural network, the system achieves accurate material and texture recognition, enhancing the capability of ...
Youning Duo, Jinxi Duan, Xinyu Hao, Kaining Tao, Xin Liu, Mingyuan Li, Xingyu Chen, Wenbo Liu, Zonghao Zuo, Li Wen   +9 more
wiley   +1 more source

EMG‐Driven Telemetry and Inference System for Fish: Pose Reconstruction and Flow Sensing

Advanced Intelligent Systems, EarlyView.
This work introduces an electromyography (EMG)‐driven telemetry framework that reconstructs body pose and infers hydrodynamic conditions in freely swimming fish. A custom 16‐channel archival system records intramuscular EMG, enabling deep‐learning models to decode joint kinematics, classify flow regimes, and reveal channel‐efficient sensing strategies.
Rahdar Hussain Afridi, Waqar Hussain Afridi, Muhammad Hamza, Ahsan Tanveer, Mingxin Wu, Xingwen Zheng, Liang Li, Guangming Xie   +7 more
wiley   +1 more source

Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)

Advanced Intelligent Systems, EarlyView.
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley   +1 more source

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Hierarchical multi-agent reinforcement learning

Autonomous Agents and Multi-Agent Systems, 2001
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL.
Ghavamzadeh, M, Mahadevan, S, Makar, R
openaire   +1 more source

Multi-Agent Inverse Reinforcement Learning

2010 Ninth International Conference on Machine Learning and Applications, 2010
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multi-agent inverse reinforcement learning, where reward functions of multiple agents are learned by observing their uncoordinated ...
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik   +5 more
openaire   +1 more source

Gifting in Multi-Agent Reinforcement Learning

International Joint Conference on Autonomous Agents and Multiagent Systems, 2020
Multi-agent reinforcement learning has generally been studied under an assumption inherited from classical reinforcement learning: that the reward function is the exclusive property of the environment, and is only altered by external factors. In this work, we break free of this assumption and introduce peer rewarding, in which agents can deliberately ...
Andrei Lupu, Doina Precup
openaire   +2 more sources

Multi-Agent Reinforcement Learning

2020
In reinforcement learning, complicated applications require involving multiple agents to handle different kinds of tasks simultaneously. However, increasing the number of agents brings in the challenges on managing the interactions among them. In this chapter, according to the optimization problem for each agent, equilibrium concepts are put forward to
Huaqing Zhang, Shanghang Zhang
openaire   +3 more sources

Multi-Agent Patrolling with Reinforcement Learning

International Joint Conference on Autonomous Agents and Multiagent Systems, 2004
Patrolling tasks can be encountered in a variety of real-world domains, ranging from computer network administration and surveillance to computer wargame simulations. It is a complex multi-agent task, which usually requires agents to coordinate their decision-making in order to achieve optimal performance of the group as a whole. In this paper, we show
Santana, Hugo, Ramalho, Geber, Corruble, Vincent, Ratitch, Bohdana   +3 more
openaire   +3 more sources

The Dynamics of Multi-Agent Reinforcement Learning

2010
Infinite-horizon multi-agent control processes with non-determinism and partial state knowledge have particularly interesting properties with respect to adaptive control, such as the non-existence of Nash Equilibria (NE) or non-strict NE which are nonetheless points of convergence.
Luke Dickens, Krysia Broda, Alessandra Russo   +2 more
openaire   +2 more sources