Pose Aware Object Manipulation

Pose-aware object manipulation (POM) in robotics focuses on enabling robots to grasp and manipulate objects effectively, considering their 6D pose (position and orientation) in diverse environments. Current research emphasizes developing robust models, often leveraging deep reinforcement learning and generative models, to handle pose variations and uncertainties, including challenges like cluttered scenes and articulated objects. Benchmarks like ManiPose are crucial for evaluating and comparing these models, highlighting the need for improved generalization across different object properties and environmental conditions. Advances in POM are vital for creating more adaptable and versatile robots capable of performing complex tasks in real-world settings.