Abstract: Preference-based reinforcement learning (PBRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries make it possible to learn policies aligned with human preferences, PBRL suffers from low query efficiency, as policy bias limits trajectory diversity and reduces the number of … [Read more...] about DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition
Reinforcement learning
AINav
Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive … [Read more...] about AINav
Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks: Gripper Design Boosts Learning
Abstract: In this work, we investigate how a state-of-the-art grasp planner based on deep reinforcement learning performs when applied to a soft–rigid gripper in a decluttering task. The gripper, called Soft ScoopGripper (SSG), is endowed with a rigid scoop-shaped part that facilitates the interaction with the environment and with objects. We hypothesize that the clever … [Read more...] about Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks: Gripper Design Boosts Learning
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design
Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually rely on pre-existing reference trajectories obtained by capturing animal motions or transferring experience from existing controllers. This work aims to prove that learning dynamic jumping is … [Read more...] about Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design
Reinforcement Learning for High-Speed Quadrupedal Locomotion With Motor Operating Region Constraints: Mitigating Motor Model Discrepancies through Torque Clipping in Realistic Motor Operating Region
This article presents a method for achieving high-speed running of a quadruped robot by considering the actuator torque–speed operating region in reinforcement learning. The physical properties and constraints of the actuator are included in the training process to reduce state transitions that are infeasible in the real world due to motor torque–speed limitations. The gait … [Read more...] about Reinforcement Learning for High-Speed Quadrupedal Locomotion With Motor Operating Region Constraints: Mitigating Motor Model Discrepancies through Torque Clipping in Realistic Motor Operating Region
