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Planning

Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm

April 17, 2026 by Haoran Xu Xianwei Chen Yilin Lang Qinyuan Ren College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China

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

Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm

April 17, 2026 by Haoran Xu Xianwei Chen Yilin Lang Qinyuan Ren

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

April 17, 2026 by Enrico Turco Valerio Bo Chiara Castellani Gionata Salvietti Monica Malvezzi Domenico Prattichizzo

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

Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle

September 10, 2025 by Yingbing Chen, Jie Cheng, Sheng Wang, Hongji Liu, Xiaodong Mei, Xiaoyang Yan, Mingkai Tang, Ge Sun, Ya Wen, Junwei Cai, Xupeng Xie, Lu Gan, Mandan Chao, Ren Xin, Lujia Wang, Ming Liu, Jianhao Jiao

In recent years, the rapid evolution of autonomous vehicles (AVs) has reshaped global transportation systems, leading to an increase in autonomous shuttle applications in people’s daily lives. Leveraging the accomplishments of our earlier endeavor, particularly Hercules (Liu et al., 2021), an autonomous logistics vehicle for transporting goods, we introduce Snow Lion, an … [Read more...] about Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle

Motion Planning for Humanoid Locomotion: Applications to Homelike Environments

March 18, 2025 by George Mesesan, Robert Schuller, Johannes Englsberger, Máximo A. Roa, Jinoh Lee, Christian Ott, Alin Albu-Schäffer

“What can your humanoid robot do?” is probably the most commonly asked question that we, as roboticists, have to answer when interacting with the general public. Often, the question is framed in the familiar household or office setting, with implied expectations of robust locomotion on uneven and cluttered terrain and compliant interaction with people, objects, and the … [Read more...] about Motion Planning for Humanoid Locomotion: Applications to Homelike Environments

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About the Magazine

As the flagship magazine of the IEEE Robotics and Automation Society, IEEE Robotics and Automation Magazine (RAM) covers the latest developments in robotics and automation. Its scope ranges from cutting-edge technological advances to emerging social, economic, ethical, and policy issues shaping the field.  Published quarterly (March, June, September, and December), RAM features both high-impact original research articles written in an engaging and accessible style, as well as reviews, columns and opinion pieces addressing a wide range of timely topics.

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IEEE Robotics & Automation Magazine  publishes four issues per year: March, June, September and December.