Robotic in Hand Manipulation

Robotic in-hand manipulation aims to enable robots to dexterously manipulate objects within their grasp, mirroring human dexterity. Current research heavily emphasizes learning-based approaches, particularly reinforcement learning, often coupled with advanced tactile sensing and state estimation techniques to overcome the challenges of complex object geometries and limited sensory information. These advancements are crucial for improving robot adaptability in unstructured environments and expanding their capabilities in various applications, such as manufacturing, healthcare, and service robotics. The development of robust and efficient algorithms, combined with improved tactile sensors, is driving progress towards more versatile and reliable robotic manipulation.