Contact Rich Task

Contact-rich tasks, involving robots interacting with environments through physical contact, are a central challenge in robotics research, focusing on developing robust and adaptable control strategies for manipulation and assembly. Current research emphasizes learning-based approaches, including reinforcement learning and differentiable optimization methods, to improve pose estimation and control in the presence of uncertainty and contact constraints, often incorporating multi-modal sensor fusion (vision, force, proprioception) and hierarchical control architectures. These advancements are crucial for enabling robots to perform complex tasks in unstructured environments, with significant implications for manufacturing, assistive robotics, and human-robot collaboration.