Automated Robotic Data Generation
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs
arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of human demonstrations (as few as one) into a large robot dataset for imitation learning. Our approach decomposes demonstration generation into two steps: (1) offline demonstration annotation that extracts keyframes, salient objects, and pose-object relations; and (2) online keypose retargeting...
Nvidia Cosmos 3
Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks. NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning, world generation, and action generation within a single open model.
IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking
arXiv:2606.08729v1 Announce Type: new Abstract: Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custom code or complex interfaces, creating a barrier to rapid prototyping and automated algorithm development. To this end, we propose the Intelligent Robot Simulator (IR-SIM), a lightweight skill-native navigation simulator designed for rapid scenario construction, benchmarking, and robot learning.
GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation
arXiv:2606.03682v1 Announce Type: new Abstract: Embodied navigation connects intelligent agents with the physical world and is fundamental for general robotic intelligence. Limited availability and quality of navigation data have constrained Vision-and-Language Navigation (VLN) systems' generalization and long-horizon capabilities. To address this, we curate diverse 3D scenes and develop an automated pipeline for large-scale navigation data, resulting in the GN-Matrix dataset.
SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
Announce Type: new Abstract: Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus primarily on table-top or wheeled robots. A scalable and reproducible benchmark for whole-body humanoid loco-manipulation remains an open problem.
Amazon unveils latest warehouse robot as tech giants continue AI layoffs
Amazon has unveiled its latest warehouse robot that can take commands in conversational language, underscoring how AI-powered automation is advancing as companies continue to slash their corporate workforce in AI-driven efficiencies. The tech giant's next-generation Proteus is an autonomous mobile robot, which is designed to understand natural language commands from workers and transport items in warehouses. It was launched at the company's Delivering the Future event in London on Thursday.
AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
arXiv:2606.06155v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the learning of precise perception--action mappings. To address this challenge, we propose \textbf{AffordanceVLA}, a unified framework that introduces structured affordance...
Superintelligence: The Idea That Eats Smart People (2016)
This is the text version of a talk I gave on October 29, 2016, at Web Camp Zagreb [video] (45 mins) SuperintelligenceThe Idea That Eats Smart People | | | In 1945, as American physicists were preparing to test the atomic bomb, it occurred to someone to ask if such a test could set the atmosphere on fire. This was a legitimate concern.
Folding Beijing
At ten of five in the morning, Lao Dao crossed the busy pedestrian lane on his way to find Peng Li. After the end of his shift at the waste processing station, Lao Dao had gone home, first to shower and then to change. He was wearing a white shirt and a pair of brown pants—the only decent clothes he owned.
When AI Builds Itself: Our progress toward recursive self-improvement
For most of AI’s history, humans drove every step in its development cycle. But at Anthropic, we are delegating a growing share of AI development to AI systems themselves, which is speeding up our work. Taken far enough, and given enough compute, that trend points to an AI system capable of fully autonomously designing and developing its own successor.