This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...

A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

Deepreinforcement learning has disadvantages such as low sample utilization and slow convergence, and thousandsof trial-and-error iterations are required to perform ...

A new machine-learning technique can train and control a reconfigurable soft robot that can dynamically change its shape to complete a task. The researchers also built a simulator that can evaluate ...

Boasting a sophisticated design tailored for versatile mobility, Cassie demonstrates remarkable agility as it effortlessly navigates quarter-mile runs and performs impressive long jumps without ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...