The field of robotic manipulation is moving towards greater autonomy and adaptability in unstructured environments. Researchers are exploring the use of tactile sensing and reinforcement learning to improve object pose estimation and grasping performance. Additionally, there is a growing interest in developing more efficient and accurate methods for 6D pose estimation, including the use of monocular depth estimation and RGB-based tracking. These advancements have the potential to significantly improve the capabilities of robotic systems in a variety of applications, including pick-and-place tasks, assembly, and manipulation of dynamic objects. Noteworthy papers in this area include:

• Improving Robotic Manipulation: Techniques for Object Pose Estimation, which proposes the use of tactile sensing and reinforcement learning for object pose estimation and disassembly tasks.
• RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking, which introduces a novel framework for real-time 6D pose estimation and tracking using RGB data.
• Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping, which proposes a novel framework for recovering metric depth from a single RGB image.