Autonomous robotic manipulation for grasping a target object in cluttered environments

This work addresses the challenge of grasping a target object in cluttered environments, even when it is partially visible or fully occluded. The proposed approach enables the manipulator to learn a sequence of strategic pushing actions that rearrange the scene to make the target object graspable. Our pipeline integrates image morphological processing with deep reinforcement learning (DRL), using GR-ConvNet to predict grasp points for the target. When the object is considered ungraspable, a soft actor-critic (SAC) model guides optimal pushing actions. A novel clutter map is introduced, encoding environmental clutter into a quantitative score that informs the decision-making process. The system shows improved performance with a discount factor (\(\gamma \)) of 0.9, demonstrated through comparative analysis with and without the clutter map. We also compare models trained in discrete versus continuous action spaces to evaluate the impact of action space on DRL effectiveness. The pipeline generalizes well to diverse objects and integrates directly with hardware, requiring no additional training for real-world deployment.