Planar Robot

Planar robot research focuses on developing control strategies and algorithms for robots operating in a two-dimensional plane, addressing challenges such as redundancy resolution, complex actuation models (e.g., in soft robotics), and safe human-robot interaction. Current research emphasizes model-based control, incorporating techniques like control barrier functions and Gaussian process regression to handle uncertainties and ensure safety, alongside learning-based approaches such as imitation learning and deep reinforcement learning with graph neural networks for improved efficiency and adaptability. These advancements are significant for improving robot dexterity, enabling complex tasks like teleoperation and multi-agent coordination, and facilitating safer and more efficient robotic systems in various applications.