Designing Grasp Planning and Force Control for a Real Robot Arm

Designing a grasp planning and force control stack for a real robot arm involves more than picking algorithms. The sensors you choose, the gripper you mount, the planning approach you apply, and the way you handle uncertainty all shape whether the system grasps reliably or drops objects unexpectedly. This course walks through those decisions in a structured way. You will work through written design exercises that mirror how a robotics engineering team would plan a manipulation stack. The emphasis is on the practical tradeoffs that matter when robots have to work with unknown objects in changing environments. What you'll learn: - Choose end-effectors and sensors appropriate for specific manipulation tasks - Design grasp planning pipelines including geometric, sampling-based, and learning-based methods - Apply force and impedance control to handle delicate, constrained, or contact-rich tasks - Build perception pipelines that combine depth, color, and tactile cues for robust grasping - Plan recovery behaviors when grasps slip, miss, or apply unsafe forces - Design integration with motion planning, behavior systems, and safety monitoring The course progresses from end-effector and sensor choice through grasp planning, force control, perception, and recovery. A capstone written exercise asks you to draft a one-page design for a manipulation stack targeted at a specific robot and task domain. This course is designed for robotics engineers, computer vision practitioners, and students of mechatronics with some software background. No prior manipulation experience is required. The course treats the stack as a design problem you can reason about on paper before any hardware is selected.