InfoQ Homepage Robotics Content on InfoQ
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Physical Intelligence Unveils Robotics Foundation Model Pi-Zero
Physical Intelligence recently announced π0 (pi-zero), a general-purpose AI foundation model for robots. Pi-zero is based on a pre-trained vision-language model (VLM) and outperforms other baseline models in evaluations on five robot tasks.
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Hugging Face Unveils LeRobot, an Open-Source Machine Learning Model for Robotics
Hugging Face has unveiled LeRobot, a new machine learning model trained for real-world robotics applications. LeRobot functions as a platform, offering a versatile library for data sharing, visualization, and training of advanced models.
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Nvidia Announces Robotics-Oriented AI Foundational Model
At its recent GTC 2024 event, Nvidia announced a new foundational model to build intelligent humanoid robots. Dubbed GR00T, short for Generalist Robot 00 Technology, the model will understand natural language and be able to observe human actions and emulate human movements.
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Researchers at Stanford Use Brain Signals to Control Intelligent Robots
In a paper presented at the 7th Annual Conference on Robot Learning last November, a team of Stanford University researchers presented an intelligent human brain-robot interface that enables controlling a robot through brain signals. Dubbed NOIR, short for Neural Signal Operated Intelligent Robots, the system uses electroencephalography (EEG) to communicate human intentions to the robots.
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Nvidia Introduces Eureka, an AI Agent Powered by GPT-4 That Can Train Robots
Nvidia Research revealed that it has created a brand-new AI agent named Eureka that is driven by OpenAI's GPT-4 and is capable of teaching robots sophisticated abilities on its own.
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Google DeepMind Announces LLM-Based Robot Controller RT-2
Google DeepMind recently announced Robotics Transformer 2 (RT-2), a vision-language-action (VLA) AI model for controlling robots. RT-2 uses a fine-tuned LLM to output motion control commands. It can perform tasks not explicitly included in its training data and improves on baseline models by up to 3x on emergent skill evaluations.
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Google's PaLM-E Combines Vision and Language AI for Robot Control
Researchers from Google's Robotics team recently announced PaLM-E, a combination of their PaLM and Vision Transformer (ViT) models designed for controlling robots. PaLM-E handles multimodal input data from robotic sensor and outputs text commands to control the robot's actuators. Besides performing well on several robotics tasks, PaLM-E also outperforms other models on the OK-VQA benchmark.
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NVIDIA Open-Sources Robot Learning Framework Orbit
A team of researchers from NVIDIA, ETH Zurich, and the University of Toronto open-sourced Orbit, a simulation-based robot learning framework. Orbit includes wrappers for four learning libraries, a suite of benchmark tasks, and simulation for several robot platforms, as well as interfaces for deploying trained agents on physical robots.
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Microsoft Wants to Use ChatGPT to Control Robots through Language
In a recent paper, researchers at Microsoft Autonomous Systems and Robotics Group showed how OpenAI's ChatGPT can be used for robotics applications, including how to design prompts and how to direct ChatGPT to use specific robotic libraries to program the task at hand.
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Google's Code-as-Policies Lets Robots Write Their Own Code
Researchers from Google's Robotics team have open-sourced Code-as-Policies (CaP), a robot control method that uses a large language model (LLM) to generate robot-control code that achieves a user-specified goal. CaP uses a hierarchical prompting technique for code generation that outperforms previous methods on the HumanEval code-generation benchmark.
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Google Open-Sources Natural Language Robot Control Method SayCan
Researchers from Google's Robotics team have open-sourced SayCan, a robot control method that uses a large language model (LLM) to plan a sequence of robotic actions to achieve a user-specified goal. In experiments, SayCan generated the correct action sequence 84% of the time.
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Berkeley Researchers Announce Robot Training Algorithm DayDreamer
Researchers from University of California, Berkeley, recently announced DayDreamer, a reinforcement-learning (RL) AI algorithm that uses a world model, which allows it to learn more quickly without the need for interacting with a simulator. Using DayDreamer, the team was able to train several physical robots to perform complex tasks within only a few hours.
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Amazon Launches AWS IoT RoboRunner for Robot Fleet Management Applications
Amazon recently announced the preview of AWS IoT RoboRunner, a new service to help companies build and deploy robotics management applications. Developed from technology already in use at Amazon warehouses, IoT RoboRunner provides infrastructure to connect fleets of robots and automation software.
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Joanneum Research Releases Robot AI Platform Robo-Gym Version 1.0.0
Joanneum Research's Institute for Robotics and Mechatronics has released version 1.0.0 of robo-gym, an open-source framework for developing reinforcement learning (RL) AI for robot control. The release includes a new obstacle avoidance environment, support for all Universal Robots cobot models, and improved code quality.
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MIT Announces AI Benchmark ThreeDWorld Transport Challenge
A team of researchers from MIT and the MIT-IBM Watson AI Lab have announced the ThreeDWorld Transport Challenge, a benchmark task for embodied AI agents. The challenge is to improve research on AI agents that can control a simulated mobile robot that is guided by computer vision to pick up objects and move them to new locations.