DRL (Deep Reinforcement Learning) has become a research hotspot in the trajectory planning of robot arms, enabling robots to show behaviors close to humans in tasks such as grasping, opening doors, ...

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 ...

Reinforcement learning (RL) for robotics is often associated with large GPU clusters, distributed infrastructure, and x86-based development environments. Training a humanoid robot with high-fidelity ...

Autonomous vehicles (AVs) have the potential to transform transportation systems by improving safety, efficiency, accessibility, and comfort. However, developing reliable control policies for AVs to ...

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 ...

Figure AI has developed a new humanoid robotic natural walking capability for its humanoid robots, leveraging reinforcement learning (RL) and simulation-based training. This approach enables the ...

Continual robot learning is an emerging interdisciplinary field that integrates advances from machine learning, robotics, and cognitive science to build ...

Using a bunch of carrots to train a pony and rider. (Photo by: Education Images/Universal Images Group via Getty Images) Andrew Barto and Richard Sutton are the recipients of the Turing Award for ...

Recent advancements in robotics have seen the development of machines that can closely replicate human intelligence and movement, unlocking their potential for analyzing data, performing physical ...

Summarizing the Company’s commercialization path, Dr. Yang underscored the value of an open ecosystem: “In the era of AI computing, the commercial competitive advantage is not just silicon performance ...

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a cornerstone of intelligence for machines and living ...

Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.