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Articles by Asawaree Bhide
Develop Physical AI Reasoning, World, and Action Models with NVIDIA Cosmos 3 Original
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.
R²D²: Improving Robot Manipulation with Simulation and Language Models
Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this, gaps between simulation and reality, and non-optimized grippers or tools often limit how reliably robots can generalize, execute long-horizon tasks, and achieve human-level dexterity across diverse tasks. This edition of NVIDIA Robotics Research and Development Digest (R²D²) explores novel approaches to improving robot manipulation skills.
Build and Orchestrate End-to-End SDG Workflows with NVIDIA Isaac Sim and NVIDIA OSMO
As robots take on increasingly dynamic mobility tasks, developers need physics-accurate simulations that translate across environments and workloads. Training robot policies and models to do these tasks requires a large amount of diverse, high-quality data, which is often expensive and time-consuming to collect in the physical world. Therefore, generating synthetic data at scale using cloud technology is essential to accelerate physical AI.
R²D²: Boost Robot Training with World Foundation Models and Workflows from NVIDIA Research
As physical AI systems advance, the demand for richly labeled datasets is accelerating beyond what we can manually capture in the real world. World foundation models (WFMs), which are generative AI models trained to simulate, predict, and reason about future world states based on the dynamics of real-world environments, can help overcome this data challenge. NVIDIA Cosmos is a platform for WFM development for physical AI, like robotics and autonomous vehicles.
R²D²: Adapting Dexterous Robots with NVIDIA Research Workflows and Models
Robotic arms are used today for assembly, packaging, inspection, and many more applications. However, they are still preprogrammed to perform specific and often repetitive tasks. To meet the increasing need for adaptability in most environments, perceptive arms are needed to make decisions and adjust behavior based on real-time data. This leads to more flexibility across tasks in collaborative environments, and improves safety through hazard awareness.
R²D²: Advancing Robot Mobility and Whole-Body Control with Novel Workflows and AI Foundation Models from NVIDIA Research
Welcome to the first edition of the NVIDIA Robotics Research and Development Digest (R2D2). This technical blog series will give developers and researchers deeper insight and access to the latest physical AI and robotics research breakthroughs across various NVIDIA Research labs. Developing robust robots presents significant challenges, such as: Data Scarcity: Generating diverse, real-world training data for AI models.
Scale Synthetic Data and Physical AI Reasoning with NVIDIA Cosmos World Foundation Models
The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and representative datasets, these systems don’t get proper training and face testing risks due to poor generalization, limited exposure to real-world variations, and unpredictable behavior in edge cases. Collecting massive real-world datasets for training is expensive, time-intensive, and often constrained by possibilities.
Advancing Humanoid Robot Sight and Skill Development with NVIDIA Project GR00T
Humanoid robots present a multifaceted challenge at the intersection of mechatronics, control theory, and AI. The dynamics and control of humanoid robots are complex, requiring advanced tools, techniques, and algorithms to maintain balance during locomotion and manipulation tasks.
Create, Design, and Deploy Robotics Applications Using New NVIDIA Isaac Foundation Models and Workflows
The application of robotics is rapidly expanding in diverse environments such as smart manufacturing facilities, commercial kitchens, hospitals, warehouse logistics, and agricultural fields. The industry is shifting towards intelligent automation, which requires enhanced robot capabilities to perform functions including perception, mapping, navigation, load handling, object grasping, and intricate assembly tasks. AI plays a pivotal role in this evolution, enhancing robotic performance.
Generate Synthetic Data for Deep Object Pose Estimation Training with NVIDIA Isaac ROS
For robotic agents to interact with objects in their environment, they must know the position and orientation of objects around them. This information describes the six degrees of freedom (DOF) pose of a rigid body in 3D space, detailing the translational and rotational state. Accurate pose estimation is necessary to determine how to orient a robotic arm to grasp or place objects in a specific way.
Boosting Custom ROS Graphs Using NVIDIA Isaac Transport for ROS
NVIDIA Isaac Transport for ROS (NITROS) is the implementation of two hardware-acceleration features introduced with ROS 2 Humble-type adaptation and type negotiation. Type adaptation enables ROS nodes to work in a data format optimized for specific hardware accelerators. The adapted type is used by processing graphs to eliminate memory copies between the CPU and the memory accelerator.
Detecting Objects in Point Clouds Using ROS 2 and TAO-PointPillars
Accurate, fast object detection is an important task in robotic navigation and collision avoidance. Autonomous agents need a clear map of their surroundings to navigate to their destination while avoiding collisions. For example, in warehouses that use autonomous mobile robots (AMRs) to transport objects, avoiding hazardous machines that could potentially damage robots has become a challenging problem.
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