UCLA

The goal of this work is to formulate algorithms that can address three key ingredients I believe are necessary towards making robots autonomous and smart: (1) The robot needs to be able to react in an energy-efficient manner to outside disturbances; (2) The robot needs to understand its location of its surroundings and evaluate the uncertainty of its location to optimally and safely achieve some goal state; (3) The robot should continuously learn from experience during operation. Throughout this prospectus, we show algorithms that can achieve in obtaining these ingredients. First, we will demonstrate a simple algorithm that plans for the most energy-efficient trajectories for a quadruped robot by optimizing for parameters such as cost of transport, manipulability measures, and avoid non-slipping configurations. With this algorithm, we show that the robot only moves when necessary, anddemonstrates behaviors of reacting to outside disturbances to ensure it does not fall while also not wasting unnecessary energy. The idea of understanding is demonstrated through an algorithm that combines an MPC, SLAM, RNN, and object detection using CNNs to generate paths for unknown and uncertain environments. This algorithm is evaluated not only for a complex quadruped robot, but also for multi-agent robot teams consisting of a UGV and UAV. The feasibility of such complex algorithm is also evaluated. Lastly, the idea of continuous learning is addressed not only through use of learning-based algorithms such as RNNs, but also through auto-tuning algorithms that employ an UKF. Using a UKF, we show that we can automatically tune controller gains and even parameters of an online planner. Because the UKF can adapt parameters quickly and without heavy computational load, the robot can continuously adapt its control/planner parameters during online operation to continually learn from the environment. To summarize, we will first present a simple planner for energy-efficient locomotion, provide two examples of end-to-end frameworks for motion planning and state estimation that uses a hybrid approach consisting of model and learning-based methods, and then provide a method of calibrating such end-to-end frameworks (which often contain many various modules) through an auto-tuning technique. Lastly, I end with a discussion on Large Language Models, and how they may potentially affect the robotic field, and further contribute to the idea of understanding in significant ways.