Results 91 to 100 of about 9,252,589 (289)

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning

, 2023
737742Safe navigation is one of the steps necessary for achieving autonomous control of robots. Among different algorithms that focus on robot navigation, Reinforcement Learning (and more specifically Deep Reinforcement Learning) has shown impressive ...
Ning, Xiangyu, Günnemann, Stephan, Rasheed, Hassan, Roscher, Karsten, Schmoeller da Roza, Felippe   +4 more
core   +1 more source

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Online Steady-State Scheduling of New Power Systems Based on Hierarchical Reinforcement Learning

Shanghai Jiaotong Daxue xuebao
With the construction of new power systems, the stochasticity of high-proportion renewable energy significantly increases the uncertainty in the operation of the power grid, posing severe challenges to its safe, stable, and economically efficient ...
ZHAO Yingying, QIU Yue, ZHU Tianchen, LI Fan, SU Yun, TAI Zhenying, SUN Qingyun, FAN Hang
doaj   +1 more source

Kernel-Based Safe Exploration in Deep Reinforcement Learning

Accepted at L4DC Conference (22 Jan 2026)
Majumdar, Rupak, Singh, Nikhil, Soudjani, Sadegh   +2 more
openaire   +2 more sources

Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

International Journal of Intelligent Machines and Robotics
This paper is under review by Int. J.
Taheri, Hamid, Hosseini, Seyed Rasoul, Nekoui, Mohammad Ali   +2 more
openaire   +3 more sources

Probability Matching and Reinforcement Learning* [PDF]

Probability matching occurs when an action is chosen with a frequency equivalent to the probability of that action being the best choice. This sub-optimal behavior has been reported repeatedly by psychologist and experimental economist.
Javier Rivas
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

Safe Deep Reinforcement Learning: Enhancing the Reliability of Intelligent Systems.

, 2023
In the last few years, the impressive success of deep reinforcement learning (DRL) agents in a wide variety of applications has led to the adoption of these systems in safety-critical contexts (e.g., autonomous driving, robotics, and medical applications), where expensive hardware and human safety can be involved.
openaire   +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