CS 6751 - Robot Manipulation
(crosslisted) MAE 6730
Spring. 4 credits. Letter grades only.
Prerequisite: MATH 4310 or equivalent, MATH 4720, proficiency in C++ or Python. Recommended prerequisite for undergraduates: MAE 4180 or CS 4750/CS 5750/MAE 4760/ECE 4770. Familiarity with ROS is required. Intended for: graduate students, or permission of the instructor. Undergraduates should have taken a previous robotics course such as MAE 4180 or CS 4750 /CS 5750 /MAE 4760 /ECE 4770 . A background in mathematics is required, especially linear algebra (e.g. MATH 4310 ) and probability (e.g. MATH 4720 ).
Robot manipulation is the ability for a robot to interact physically with objects in the world and manipulate them towards completing a task. It is one of the greatest technical challenges in robotics, due primarily to the interplay of uncertainty about the world and clutter within it. As robots become integrated into complex human environments, robot manipulation is increasingly necessary to assist humans in these unstructured environments. Robotic manipulation will enable applications like personal assistant robots in the home and factory worker robots in advanced manufacturing. This course covers the fundamental theory, concepts, and systems of robot manipulation, including both software and hardware.
Topics we will cover this semester include robot arm kinematics and dynamics, state estimation techniques such as particle filters, motion planning and combined task and motion planning, controls, human-robot interaction, and introspection. The course features a semester-long project in which each student must deliver a component that functions with several other students’ components to form a working robotic system. The scope of possible components is quite broad and extends beyond traditional robotics issues into other aspects of CS. This course is offered to prepare a student for Ph.D. research in robotics.
Note that for the CS Ph.D. breadth requirement, this course counts as the AI area with either the systems or applied research style.
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