Abstract: This paper presents a novel approach to forest habitat monitoring using robotics and advanced data analysis techniques. We introduce a quadrupedal robot with LiDAR and onboard cameras to collect detailed data about forest structure and composition. The data is then processed using a combination of data analysis techniques and machine learning algorithms to … [Read more...] about Harnessing Robotics for European Union Forest Habitats Monitoring: Toward a Robotic-Assisted Framework for Standardized Field Surveys
Quadrupedal robots
DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition
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
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
Learning Perceptive Legged Robot Locomotion in the Real World: A Systematic Review
Abstract: Perception is essential for legged locomotion as it enables robots to anticipate upcoming terrains and obstacles, facilitating adaptive traversal of challenging environments. Recent advancements in learning methodologies and legged locomotion have fostered research on the integration of perception into legged robot locomotion controllers, allowing them to operate … [Read more...] about Learning Perceptive Legged Robot Locomotion in the Real World: A Systematic Review
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
