Cambrian-S: Teaching AI to Understand Space Like Humans Do – A Deep Dive into Spatial Supersensing Imagine asking a home robot to “find the coffee mug you saw on the kitchen counter three hours ago.” For humans, this is effortless—we maintain an implicit mental model of our environment, effortlessly tracking objects and spaces over time. For today’s AI systems, this seemingly simple task remains nearly impossible. Most video AI models excel at describing what’s directly in front of them but struggle to build persistent, structured understandings of 3D space that survive viewpoint changes, occlusions, and long time gaps. This article …
GEN-0: The Embodied Foundation Model That’s Redefining Robotics Intelligence Introduction: The Missing Piece in AI’s Evolution We’re living in an era where artificial intelligence has made staggering progress. Large language models can write poetry, solve complex problems, and hold conversations that feel remarkably human. Computer vision systems can identify objects with superhuman accuracy. Yet, when it comes to physical intelligence—the kind that allows a child to catch a ball or a chef to chop vegetables—AI has consistently fallen short. This disparity isn’t surprising to those familiar with Moravec’s Paradox, which observes that what humans find difficult (like complex mathematics) is …
FastTD3: Simple, Fast, and Powerful Reinforcement Learning for Humanoid Control Reinforcement learning has dramatically advanced robotics capabilities in recent years, particularly for humanoid control tasks that require complex movement and manipulation. However, traditional RL algorithms often suffer from long training times and implementation complexity that hinder practical application and rapid iteration. Addressing these challenges, researchers have developed FastTD3 – a high-performance variant of the Twin Delayed Deep Deterministic Policy Gradient algorithm specifically optimized for complex humanoid control tasks. What makes FastTD3 remarkable isn’t algorithmic complexity but rather its strategic combination of proven techniques that deliver unprecedented training speeds without sacrificing …
GraspGen Explained: A Friendly Guide to 6-DOF Robot Grasping for Everyone A Diffusion-based Framework for 6-DOF Grasping “ How a new open-source framework lets robots pick up almost anything—without weeks of re-engineering. 1. Why Better Grasping Still Matters Pick-and-place sounds simple, yet warehouse robots still drop mugs, kitchen assistants miss forks, and lunar rovers struggle with oddly shaped rocks. Three stubborn problems keep coming back: Different grippers → one change of hardware and yesterday’s code is useless. Cluttered scenes → toys on a rug, tools in a drawer; the camera never sees the whole object. Unknown objects → you can’t …
Revolutionizing Robotic Control: How Large Language Models Solve Inverse Kinematics Challenges Robotic Arm Analysis Introduction: The New Era of Robotic Programming Inverse kinematics (IK) calculation – the process of determining joint parameters to achieve specific end-effector positions – has long been the cornerstone of robotic control. Traditional methods required manual mathematical derivation, a process both time-consuming and error-prone. Our open-source project introduces a paradigm shift by leveraging Large Language Models (LLMs) to automate this complex computational task. Core Functionality Breakdown Five Intelligent Solving Modes id: solving-modes-en name: Solving Modes Diagram type: mermaid content: |- graph TD A[Start Solving] –> B{Existing …