New research demonstrates a method for robots to learn complex tasks from limited human demonstrations by focusing on predicting 3D scene flow and leveraging cropped point cloud data.
A new autonomous agent leverages the power of natural language to navigate vast scientific literature and accelerate the discovery of materials with targeted properties.
Researchers have developed a reinforcement learning system that allows two quadruped robots to collaboratively perform complex jumping maneuvers, pushing the limits of robotic teamwork.
Successfully integrating intelligent agents into the workplace demands a strategic shift towards workflow design and human oversight, rather than simply deploying the latest AI technology.
New research explores how deep learning can equip social robots with the ability to predict and respond to human gaze, fostering more natural and effective interactions.
![A significant chasm exists between the streamlined, computationally-focused workflows currently dominating early-stage drug discovery-reliant on representations like [latex]SMILES[/latex] strings, curated databases, and text mining-and the complex, multi-modal biological realities of iterative wet lab experimentation and multi-objective optimization inherent in bringing therapies to fruition, a limitation this work seeks to address through architectural innovation.](https://arxiv.org/html/2602.10163v1/x1.png)
A new review assesses the current limitations of AI-powered drug discovery systems and charts a path toward more effective, agentic frameworks.
Researchers have developed an underwater robot that mimics the graceful movements of a manta ray, offering a new approach to efficient and stable autonomous exploration.
As artificial intelligence tools become increasingly prevalent in research, a critical examination of their impact on core scientific principles is urgently needed.
A new framework breaks down human demonstrations into manageable steps, paving the way for more intuitive robot collaboration.
Researchers have developed a multi-agent system that dramatically improves the ability to extract insights from complex scientific data presented in tables and figures.