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 ...
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 ...
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Video: China’s humanoid robot masters complex skills in hours without prior setup
Last year, PNDbotics debuted its latest humanoid platforms at WAIC 2025, highlighting advances in actuation, control, and ...
Tesla is ramping up hiring for its humanoid robot program, Optimus, including some reinforcement learning engineers. It was hard to take Tesla Bot seriously when Elon Musk announced it by having ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
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 ...
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