Results 61 to 70 of about 12,894,975 (292)

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Workload-Aware Scheduling Using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks

IEEE Access, 2023
In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model,
Soohyun Park, Chanyoung Park, Soyi Jung, Jae-Hyun Kim, Joongheon Kim   +4 more
doaj   +1 more source

Bounded-parameter Markov decision processes

Artificial Intelligence, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Robert Givan, Sonia M. Leach, Thomas L. Dean   +2 more
openaire   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Robust Synchronization in Markov Decision Processes [PDF]

, 2014
We consider synchronizing properties of Markov decision processes (MDP), viewed as generators of sequences of probability distributions over states. A probability distribution is p-synchronizing if the probability mass is at least p in some state, and a sequence of probability distributions is weakly p-synchronizing, or strongly p-synchronizing if ...
Doyen, Laurent, Shirmohammadi, Mahsa, Massart, Thierry   +2 more
openaire   +4 more sources

A multicriteria competitive Markov decision process [PDF]

, 2021
The course work is devoted to A multicriteria competitive Markov decision process; proposed software implementation of their solution. The work consists of an introduction, the main part that consists of six sections, a conclusion, a list of used ...
Левченко, Іларія
core  

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

Advanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao, Hongzhou Ai, Wenjun Xu, Zude Zhou, Lihui Wang   +4 more
wiley   +1 more source

Monitored Markov Decision Processes

International Joint Conference on Autonomous Agents and Multiagent Systems
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assumption that rewards are always observable is often not applicable in real-world problems. For example, the agent may need to ask a human to supervise its actions or activate a
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour, Matthew E. Taylor, Michael Bowling   +4 more
openaire   +4 more sources

Comparing Labelled Markov Decision Processes

CoRR, 2020
35 pages, 7 ...
Stefan Kiefer, Qiyi Tang 0001
openaire   +5 more sources

Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction

Advanced Robotics Research, EarlyView.
Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith, Sunny Amatya, Seyed Yousef Soltanian, Jonathan Bush, Wenlong Zhang   +4 more
wiley   +1 more source