IEEE Robotics & Automation Magazine

Legged locomotion

Reinforcement Learning for High-Speed Quadrupedal Locomotion With Motor Operating Region Constraints: Mitigating Motor Model Discrepancies through Torque Clipping in Realistic Motor Operating Region

by

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

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

by

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

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

by

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

I-CTRL: Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning

by

Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical physical execution remains a significant challenge. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in practical applications. … [Read more...] about I-CTRL: Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning

Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion

by

Reinforcement learning (RL) offers a promising solution for controlling humanoid robots, particularly for bipedal locomotion, by learning adaptive and flexible control strategies. However, direct RL application is hindered by time-consuming trial-and-error processes, necessitating training in simulation before real-world transfer. This introduces a reality gap that degrades … [Read more...] about Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion