Legged Locomotion with Graph Neural Network Policies
Proposing structured control policies for locomotion tasks via graph neural networks based on the robot configuration morphology.
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Proposing a cooperative generative adversarial method for obtaining controllable skill sets from unlabeled datasets containing diverse state transition patterns.
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Proposing a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible.
Object Manipulation via Hierarchical Reinforcement Learning Control
Employing a hierarchical control method to achieve complex high-level tasks by integrating locomotion and manipulation skills.
Autonomous Pose Tracking with Compositional Reinforcement Learning Policies
Employing a novel compositional control structure to allow legged systems to achieve full-pose trajectory tracking in a global coordinate system by integrating learned skills.
Hierarchical Deep Reinforcement Learning for Legged Robot Navigation
Learning to utilize learned low-level skills in high-level tasks by considering only high-level objectives.
State Estimation with Particle Filter for Mobile Robot Pose Tracking
Tracking system states with general nonlinear system and general noise distributions with Particle Filter.
Wheeled Platform Integration for Search and Rescue Applications
Developing algorithmic components for a wheeled autonomous mobile platform that operates autonomously in search and rescue scenarios.
Boids Behavior Control
Developing control algorithms that simulate the flocking behavior of Boids.
Inverse Kinematics Locomotion Control for a Legged Robot
Finding desired joint angles in trajectory tracking problems with inverse kinematic control.