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Robot kinematics

A Whole-Body Integrated AVATAR System: Implementation of Telepresence With Intuitive Control and Immersive Feedback

June 24, 2025 by Sungman Park, Junsoo Kim, Hojae Lee, Minwoong Jo, Dohoon Gong, Dawon Ju, Dami Won, Sihyeon Kim, Jinhyeok Oh, Hun Jang, Joonbum Bae,

This paper proposes an intuitive and immersive whole-body teleoperation system with motion-based control and multi-modal feedback. The system consists of an anthropomorphic teleoperated robot and a haptic interface platform. The teleoperated robot has dual arms with dexterous hands, a head with a neck, a waist, giving it a human-like appearance and a large range of motion … [Read more...] about A Whole-Body Integrated AVATAR System: Implementation of Telepresence With Intuitive Control and Immersive Feedback

Toward Fully Autonomous Aviation: PIBOT, a Humanoid Robot Pilot for Human-Centric Aircraft Cockpits

March 18, 2025 by Sungjae Min, Gyuree Kang, Hyungjoo Kim, David Hyunchul Shim

Humanoid robots have been considered ideal for automating daily tasks, though most research has centered on bipedal locomotion. Many activities we do routinely, such as driving a car, require real-time system manipulation as well as substantial field-specific knowledge. Recent breakthroughs in natural language processing, particularly with large language models (LLMs), are … [Read more...] about Toward Fully Autonomous Aviation: PIBOT, a Humanoid Robot Pilot for Human-Centric Aircraft Cockpits

Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework

March 18, 2025 by Junjia Liu, Zhuo Li, Minghao Yu, Zhipeng Dong, Sylvain Calinon, Darwin Caldwell, Fei Chen

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios that require strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious … [Read more...] about Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework

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

The Open Stack of Tasks Library: OpenSoT: A Software Dedicated to Hierarchical Whole-Body Control of Robots Subject to Constraints

March 18, 2025 by Enrico Mingo Hoffman, Arturo Laurenzi, Nikos G. Tsagarakis

The Open Stack of Tasks (OpenSoT) library is a state-of-the-art framework for instantaneous whole-body motion planning and control based on quadratic programming optimization. The library is designed to enable users to easily write and solve a variety of complex instantaneous whole-body control problems with minimal input, facilitating the addition of new tasks, constraints, … [Read more...] about The Open Stack of Tasks Library: OpenSoT: A Software Dedicated to Hierarchical Whole-Body Control of Robots Subject to Constraints

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IEEE Robotics & Automation Magazine (RAM) has over 14,000 readers who are the people who drive this remarkable technology. More than half work in basic research and many of the others are top level engineers and decision-makers in industry.  This magazine highlights new concepts in Robotics and Automation that are applied to real-world systems. It delivers tutorial and survey papers by distinguished experts in the field, organizes focused special issues on hot topics, and provides a forum for disseminating and discussing emerging trends, novel achievements, and selected news relevant to the development of the whole community active in these fields worldwide.

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