Recent advances in perception, planning, and control have enabled legged robots to successfully navigate in environments that are mostly known or well-structured and modeled. The DARPA Robotics Challenge (DRC) 2015 showed that in real-world unstructured and uncertain environments robots often lack robustness with regards to locomotion. From one side, this may be due to modeling uncertainties and actuation inaccuracies that affect the control loops. From the other side, both proprioceptive and exteroceptive perception and planning are crucial for detecting foothold and handhold affordances in the environment, and generating agile motions accordingly. This workshop provides a platform for researchers from perception, planning, and control in legged robotics to disseminate and exchange ideas, evaluating their advantages and drawbacks. This includes methods for robust control/planning optimization, such as Model Predictive Control, as well as path planning and perception methods for detecting footholds and handholds on challenging surfaces for legged robots including bipeds and quadrupeds. The goal is to show various ways from sensing the environment to finding contacts and planning/controlling the body and limb trajectories for achieving agile and robust locomotion. The aim is to foster collaboration among researchers that are working on legged robots to advance the state of the art in robot locomotion.