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
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Humanoid Robot RHP Friends: Seamless Combination of Autonomous and Teleoperated Tasks in a Nursing Context
This article describes RHP Friends, a social humanoid robot developed to enable assistive robotic deployments in human-coexisting environments. As a use case application, we present its potential use in nursing by extending its capabilities to operate devices and tools according to the task and by enabling remote assistance operations. To meet a wide variety of tasks and … [Read more...] about Humanoid Robot RHP Friends: Seamless Combination of Autonomous and Teleoperated Tasks in a Nursing Context
Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework
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
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
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
