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

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.

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 ...

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 ...

The companies have jointly developed an AI robot control system that can interact with the physical world and be used in various fields from logistics to rescue operations. Tests have shown that in ...

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