Adaptive Manipulation

Adaptive manipulation in robotics focuses on enabling robots to perform manipulation tasks robustly in unpredictable environments, adapting to variations in object properties, task constraints, and unforeseen events. Current research emphasizes learning-based approaches, employing techniques like deep reinforcement learning, behavior trees, and contact-implicit model predictive control, often integrated with advanced perception systems (e.g., visual attention mechanisms, force feedback). This field is crucial for advancing robotic dexterity and autonomy, with applications ranging from industrial automation and assistive robotics to more complex tasks requiring interaction with unstructured environments. The development of generalizable and robust adaptive manipulation capabilities is a key step towards creating truly versatile robots.