Stable Manipulation

Stable manipulation in robotics aims to enable robots to reliably grasp, move, and interact with objects, overcoming challenges like unpredictable object shapes and workspace limitations. Current research focuses on improving manipulation robustness through methods such as multi-modal planning (integrating visual and other sensory data), reinforcement learning with carefully designed reward functions (e.g., dense rewards emphasizing stability), and incorporating learned models (like large language models) to correct for errors during manipulation. These advancements are crucial for deploying robots in real-world scenarios requiring dexterity and precision, impacting fields like manufacturing, surgery, and assistive technologies.