Interesting Engineering on MSN
AI-trained quadruped robot walks rough, low-friction terrain without human input
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 ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Over the past two decades, humanoid robots have greatly improved their ability to perform functions like grasping objects and using computer vision to detect things since Honda’s release of the ASIMO ...
Interesting Engineering on MSN
Video: China’s humanoid robot masters complex skills in hours without prior setup
PNDbotics unveils Adam-U Ultra, a humanoid robot with VLA AI and 10,000+ data samples, learning new skills in hours.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback