Robotics

Large Language Models (LLMs) have emerged as a powerful ally for developers, promising to revolutionize how coding tasks are approached. By serving as intelligent assistants, LLMs have the potential to streamline various aspects of the development...
Developing middleware solutions for large language models (LLMs) represents an effort to bridge AI's theoretical capabilities and its practical applications in real-world scenarios. The challenge of navigating and processing enormous quantities of data within complex environments,...

Researchers from Stanford Present Mobile ALOHA: A Low-Cost and Whole-Body Teleoperation System for Data Collection

Since it enables humans to teach robots any skill, imitation learning via human-provided demonstrations is a promising approach for creating generalist robots. Lane-following in...

This Paper Explores Efficient Predictive Control with Sparsified Deep Neural Networks

Robotics is currently exploring how to enhance complex control tasks, such as manipulating objects or handling deformable materials. This research niche is crucial as...

How do You Unveil the Power of GPT-4V in Robotic Vision-Language Planning? Meet ViLa: A Simple and Effective AI Method that Harnesses GPT-4V for...

The problem of achieving superior performance in robotic task planning has been addressed by researchers from Tsinghua University, Shanghai Artificial Intelligence Laboratory, and Shanghai...

Researchers from NYU and Meta Introduce Dobb-E: An Open-Source and General Framework for Learning Household Robotic Manipulation

The team of researchers from NYU and Meta aimed to address the challenge of robotic manipulation learning in domestic environments by introducing DobbE, a...

KAIST Researchers Introduce Quatro++: A Robust Global Registration Framework Exploiting Ground Segmentation for Loop Closing in LiDAR SLAM

The problem of sparsity and degeneracy issues in LiDAR SLAM has been addressed by introducing Quatro++, a robust global registration framework developed by researchers...

This AI Research from MIT and Meta AI Unveils an Innovative and Affordable Controller for Advanced Real-Time In-Hand Object Reorientation in Robotics

Researchers from MIT and Meta AI have developed an object reorientation controller that can utilize a single depth camera to reorient diverse shapes of...

Meet GO To Any Thing (GOAT): A Universal Navigation System that can Find Any Object Specified in Any Way- as an Image, Language, or...

A team of researchers from the University of Illinois Urbana-Champaign, Carnegie Mellon University, Georgia Institute of Technology, University of California Berkeley, Meta AI Research,...

This AI Paper from MIT Introduces a Novel Approach to Robotic Manipulation: Bridging the 2D-to-3D Gap with Distilled Feature Fields and Vision-Language Models

A team of researchers from MIT and the Institute of AI and Fundamental Interactions (IAIFI) has introduced a groundbreaking framework for robotic manipulation, addressing...

Duke University Researchers Propose Policy Stitching: A Novel AI Framework that Facilitates Robot Transfer Learning for Novel Combinations of Robots and Tasks

In robotics, researchers face challenges in using reinforcement learning (RL) to teach robots new skills, as these skills can be sensitive to changes in...

Researchers from NVIDIA and UT Austin Introduced MimicGen: An Autonomous Data Generation System for Robotics

Training robots to perform various manipulation behaviors has been made possible by imitation learning from human demonstrations. One popular method involves having human operators...

Researchers at Stanford Introduce RoboFuME: Revolutionizing Robotic Learning with Minimal Human Input

In many domains that involve machine learning, a widely successful paradigm for learning task-specific models is to first pre-train a general-purpose model from an...

Meet HITL-TAMP: A New AI Approach to Teach Robots Complex Manipulation Skills Through a Hybrid Strategy of Automated Planning and Human Control

Teaching robots complicated manipulation skills through observation of human demonstrations has shown promising results. Providing extensive manipulation demonstrations is time-consuming and labor costly, making...

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