Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-6· 0 citations· 28 references
Abstract
This paper presents a novel contamination-aware robotic manipulation framework for real-time IV bag quality assessment with adaptive manipulation policies. The system employs dual S0-ARM101 low-cost robot arms with eye-in-hand RGB vision for IV bag handling, coordinated through an open-source ROS2 architecture. Using a leader-follower architecture, the system learns visual-motor mappings from just 50 expert demonstrations, achieving 96% and 98% success rates for loading and unloading operations, respectively. The contamination-aware approach demonstrates 94% success to detected contaminated IV bags, and 90% adaptation to new scenarios, with zero safety incidents during testing. Statistical validation with 95% confidence intervals confirms the system’s reliability, while the integration of quality feedback into manipulation decision-making creates a responsive system that adapts to contamination detection results in real-time. This work demonstrates that learned visual-motor mappings with contamination awareness can significantly enhance pharmaceutical quality control while maintaining safety, establishing an economically viable framework for intelligent manufacturing automation.
The outcomes demonstrate the efficacy of combining edge intelligence with closed-loop robotic control by confirming consistent behavior throughout simulation and limited physical testing.
Xiaoming Liu, Wei Su, Jie Zhang et al.· Scientific Reports· 0 citations
A data engine which gathers data and improves its performance while executing the task, and demonstrates the ability to learn and reduce the need for expensive verifications over time, while staying within the set error-rate.
Zebin Duan, Norbert Krüger, Juan Heredia et al.· arXiv.org· 0 citations
The deployment of collaborative applications has emerged as a key trend in the manufacturing industry, driven by the synergy of human intelligence and adaptability with robots' endurance, strength, and precision. However, this closer integration of humans with robots heightens the risk of injuries. To prevent these risks, the risk reduction process is performed during design phase of collaborative workstations, ensuring that safety criteria required by current standards are met. In this work, we formulate the risk reduction process as a sequential decision-making problem and propose a Reinforcement Learning-based approach. In a simulation environment, the RL agent learns to select and parameterize prevention measures to reduce collision and crushing risks of collaborative applications. The considered prevention measures include physical barriers, laser safety barriers, and Tool Center Point (TCP) speed monitoring zones parameterized by their geometric and temporal activation interval parameters. The RL agent is trained through trial-and-error interactions within the simulated environment, aiming to maximize a reward function that prioritizes risk reduction. Secondary objectives include minimizing cycle time and reducing workstation footprint, allowing the system to derive optimized solutions that satisfy safety while balancing cost and productivity constraints. A feasibility demonstrator is presented through a use case in which the RL agent proposes prevention measures to reduce identified hazards for a single collaborative application, enabling faster human-centered design of safe collaborative workstations.
Gustavo Afonso Novak, V. Weistroffer, J. Savin et al.· AHFE International· 0 citations
The findings show that machine learning, deep learning, computer vision, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
The Global User Intent Dual-phase Estimation for Robots (GUIDER) is deployed on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine.
Nicholas Kenny, Cesar Alan Contreras, B. Ouedraogo et al.· 0 citations
This paper presents a review-oriented comparative analysis of deep reinforcement learning (DRL) for intelligent control of dual-arm robots. Instead of focusing on a single control algorithm, it organizes recent studies into an algorithm-task-metric framework and extracts quantitative evidence from representative applications including cooperative grasping, assembly, transportation, obstacle-aware planning, contact-rich control, and sim-to-real transfer. PPO, MAPPO, MADDPG, and SAC are compared in terms of success rate, convergence behavior, trajectory smoothness, force regulation, safety constraints, and transferability. Key design factors and future trends, including reward design, multimodal perception, safe reinforcement learning, sample efficiency, and real-robot deployment, are summarized to provide practical guidance for dual-arm intelligent cooperative control.
Yulong Shi, Xiangxu Sun, Shengli Zhou et al.· 2026 IEEE International Conf...· 0 citations
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