Multicontroller multiobjective locomotion planning for legged robots Different legged robot locomotion controllers offer different advantages; from speed of motion to energy, computational demand, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and subplanners, explicitly considering the multiobjective nature of the legged locomotion planning problem. The planner first obtains body paths extended with a choice of controller or subplanner, and then fills the gaps by subplanning. The method leads to paths with a mix of static and dynamic walking which only plan footsteps where necessary. We show that our approach is faster than pure footstep planning methods both in computation (2x) and mission time (1.4x), and safer than pure dynamicwalking methods. In addition, we propose two methods for aggregating the multiple objectives in searchbased planning and reach desirable tradeoffs without weight tuning. We show that they reach desirable Paretooptimal solutions up to 8x faster than fairlytuned traditional weightedsum methods. Our conclusions are drawn from a combination of planning, physics simulation, and real robot experiments.

Implementing Regularized Predictive Control for Simultaneous RealTime Footstep and Ground Reaction Force Optimization This work presents a successful implementation of a nonlinear optimizationbased Regularized Predictive Control (RPC) for legged locomotion on the MIT Cheetah 3 robot platform. Footstep placements and ground reaction forces at the contact feet are simultaneously solved for over a prediction horizon in realtime. Often in academic literature not enough attention is given to the implementation details that make the theory work in practice and many times it is precisely these details that end up being critical to the success or failure of the theory in real world applications. Nonlinear optimization for realtime legged locomotion control in particular is one of the techniques that has shown promise, but falls short when implemented on hardware systems subjected to computation limits and undesirable local minima. We discuss various algorithms and techniques developed to overcome some of the challenges faced when implementing nonlinear optimizationbased controllers for dynamic legged locomotion.