Dexterous Robot Manipulation using Physics Engine

With the advance in sensing, actuation, and mechanical design, the robots are becoming more capable of performing complex and high-precision tasks ranging from autonomous driving in urban cities to handling packages in fulfillment centers. Control and planning of robot motion in real-world environments require high-fidelity models to simulate how the robot's motion impacts physical objects before they take action. Many well-established approaches in robotics rely on mathematical models due to their analytic tractability; however as robotic systems become complex and are required to interact in physical environments, we face limitations in finding analytically tractable models and begin to resort to new models that leverage the powerful computational resources embodied in robotic systems. To address the challenges in robot operation in physical environments, in this project, we explore the idea of building computational models based on physics engines and design feedback algorithms to autotune parameters of the model whenever the robot detects the discrepancy between the simulation and its experience in the physical world -- to reduce the sim-to-real gap. As key applications, we imagine to apply outcomes of the project to enable robotic manipulators to carry out the tasks in assembly lines and research laboratories that are labor-intensive and require dexterous skills.

This project aims to adopt a physics engine to build computational model and examine the fidelity of the model through experiments with manipulators in the robotics lab at KAUST. The students are expected to collaborate with the lab members to explore creative ideas and implement them on physical robotic systems. To fulfill the requirements of the project, the students are expected to have experience working with robotic systems/software and confidence in Python programming. The model design and experiment reports are expected to be delivered at the end of the internship program.