Through a method called operant conditioning, Eunice Framm teaches her barnyard zoo animals to receive vaccines and perform ...

The rStar2-Agent framework boosts a 14B model to outperform a 671B giant, offering a path to state-of-the-art AI without ...

Abstract: Efficient and scalable radio resource allocation is essential for the success of wireless cellular networks. This paper presents a fully scalable multi-agent reinforcement learning (MARL) ...

Abstract: This study introduces a novel finite time fault tolerant controller integrating nonsingular terminal sliding mode (NTSM) and reinforcement learning (RL) strategies for manipulator systems ...

Here is how hardware security has become a major consideration in motor-control subsystems serving robotics and e-mobility.

Instead of retraining the LLM, the agent consults a dynamic store of past outcomes to make smarter decisions for new tasks.

Reinforcement learning (RL) [6] stands out as a powerful ML technique for training agents to achieve optimal behaviour in ...

NVIDIA is developing the next generation of AI by teaching it the fundamentals of human behavior, starting with something as ...

This repository is divided into two main sections. The first section, "multirotor," enables users to operate a simulated drone environment using keyboard controls. It integrates YOLOv7 with TensorRT ...

Understanding real-world videos with complex semantics and long temporal dependencies remains a fundamental challenge in computer vision. Recent progress in multimodal large language models (MLLMs) ...

Warwick primatologists, in collaboration with the Max Planck Institute, have shown that young orangutans develop their nighttime nest-building skills via observational social learning—by closely ...

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