Nature

Reinforcement learning-driven deep learning approaches for optimized robot trajectory planning

Trajectory planning and control of bipedal walking robots require precise joint torque computation to ensure stability and efficiency. Given the nonlinear dynamics and complex interactions of bipedal ...
Nature

Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains

We present a data-driven deep reinforcement learning (DRL) method for the optimization of a hierarchically structured control policy that includes the central pattern generator. This method, which is ...
Aerospace and Mechanical Insider on MSN

NVIDIA’s Doorman achieves zero-shot humanoid door opening

How could such a humanoid robot, trained fully in simulation, far outperform human operators on a real-world door-opening ...
EurekAlert!

A graph reinforcement learning framework for real-time distributed multi-robot task allocation

Effective task allocation has become a critical challenge for multi-robot systems operating in dynamic environments like search and rescue. Traditional methods, often based on static data and ...
Interesting Engineering on MSN

Watch: Korean humanoid robot performs viral K-pop dance, learns by watching videos

While China dominates humanoid robotics headlines, a Korean firm showcased a humanoid learning complex ...

Atlas Robot Masters the 'Ghost Rabona Kick'... Hyundai Motor Unveils Behind-the-Scenes Training

The training process behind how Boston Dynamics’ humanoid robot Atlas was able to demonstrate advanced soccer skills has been ...