Follow IEEE Robotics & Automation Magazine's news and updates in a matter of seconds! We will deliver any update via email, phone or you can read them from here on the site on your own news page.
You can even combine different feeds with the feed for IEEE Robotics & Automation Magazine.
Subscribing and unsubscribing is fast, easy and risk free.
The whole service is free of cost.
IEEE Robotics & Automation Magazine: IEEE Robotics & Automation Magazine -
Is this your feed? Claim it!
We present a scalable framework for cross-embodiment humanoid robot control by learning a shared latent representation that unifies motion across humans and diverse humanoid platforms, including single-arm, dual-arm, and legged humanoid robots. Our method proceeds in two stages. First, we construct a decoupled latent space that captures localized motion patterns ac...
Preference-based reinforcement learning (PBRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries make it possible to learn policies aligned with human preferences, PBRL suffers from low query efficiency, as policy bias limits trajectory diversity and reduces the number of discriminable...
Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time (RT) requirements. In this article, we propose a paradigm for generating near minimum-energy...
Learning from demonstration (LfD) has proved useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is challenging as deviations tend to accumulate, the distributional shift becomes more evident, and human teachers become fatigued over time, thereby increasing the likelihood of failure. To ad...
Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume–flow–pressure relationship in hydraulic soft actuators, focusing on low-complexity models with high accuracy. We perform regression analy...