ROBOTIC MANIPULATION MODELLING: ADVANCED TECHNIQUES FOR PRECISION GRASPING AND OBJECT HANDLING

Abstract

Robotic manipulation remains a fundamental challenge in robotics, requiring precise modeling of kinematics, dynamics, and environmental interactions. This paper presents a hierarchical control framework integrating deep reinforcement learning (DRL) for adaptive grasping, physics-based simulation for contact dynamics, and optimization techniques for motion planning. We implement our approach using PyBullet for simulation and ROS for real-world validation, demonstrating 92.3% grasp success rates across 50+ household objects. Comparative analysis shows 15% improvement over traditional model-based approaches in unstructured environments. Our results highlight the importance of combining data-driven learning with physical modeling for robust manipulation.