A Learning-Based Estimation and Control Framework for Contact-Intensive Tight-Tolerance Tasks

We propose a novel data-driven estimation and control framework for contact-rich tight tolerance tasks, which estimates the pose of the object precisely using data-driven methods and compensates for the remaining error via reinforcement learning (RL). First, the sequential particle filter estimator updates with the mixture density network (MDN), which is to represent the general non-injective conditional probability and thus is suitable for finding out the pose from the measurements including relatively low-dimensional contact wrench sensing. We further develop the RL-based fastening controller that adapts to the remaining error by optimizing the admittance gain to complete the task. The proposed framework is evaluated using an accurate real-time simulator on the bolting task and successfully transferred to an experimental environment.

Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution considering the existence of optimal gait according to the velocity for quadruped animals. In this work, we propose a hierarchical controller for quadruped robot that could generate multiple gaits (i.e. pace, trot, bound) while tracking velocity command. Our controller is composed of two policies, each working as a central pattern generator and local feedback controller, and trained with hierarchical reinforcement learning. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy, which usually shows a single gait. Codes are publicly available.