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 Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
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Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Abstract: Motion planning for autonomous vision-based car racing is a challenging task in robotics. Classical racing systems divide the task into numerous submodules, undermining computational efficiency and leading to error propagation. Previous studies have demonstrated impressive reinforcement learning (RL) results for end-to-end autonomous driving. However, RL exhibits … [Read more...] about Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
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
An Interactive Augmented Reality Interface for Personalized Proxemics Modeling: Comfort and Human–Robot Interactions
Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users’ proxemics preferences during human-robot interactions. Using an interactive augmented reality interface, we collected a set of user-preferred distances … [Read more...] about An Interactive Augmented Reality Interface for Personalized Proxemics Modeling: Comfort and Human–Robot Interactions
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
