Browsing: Robotics
Vision-Language-Action Models (VLA) for robotics are trained by combining large language models with vision encoders and then fine-tuning them on various robot datasets; this allows generalization…
Technological advancements in sensors, AI, and processing power have propelled robot navigation to new heights in the last several decades. To take robotics to the next…
In robotics, understanding the position and movement of a sensor suite within its environment is crucial. Traditional methods, called Simultaneous Localization and Mapping (SLAM), often face…
The field of deep reinforcement learning (DRL) is expanding the capabilities of robotic control. However, there has been a growing trend of increasing algorithm complexity. As…
OpenVLA: A 7B-Parameter Open-Source VLA Setting New State-of-the-Art for Robot Manipulation Policies
A major weakness of current robotic manipulation policies is their inability to generalize beyond their training data. While these policies, trained for specific skills or language…
Learning in simulation and applying the learned policy to the real world is a potential approach to enable generalist robots, and solve complex decision-making tasks. However,…
The practical application of robotic technology in automatic assembly processes holds immense value. However, traditional robotic systems have struggled to adapt to the demands of production…
The exploration of artificial intelligence within dynamic 3D environments has emerged as a critical area of research, aiming to bridge the gap between static AI applications…
Reinforcement learning has exhibited notable empirical success in approximating solutions to the Hamilton-Jacobi-Bellman (HJB) equation, consequently generating highly dynamic controllers. However, the inability to bind the…
NVIDIA’s unveiling of Project GR00T, a unique foundation model for humanoid robots, and its commitment to the Isaac Robotics Platform and the Robot Operating System (ROS)…