Loading video...

Video Failed to Load

Go Home

Just released five robotics simulation environments we have been using to train reinforcement learning from vision and touch! Very easy to install. Powered by Google DeepMind MuJoCo, Gymnasium, and robosuite. GitHub link:

carlo_sferrazza's profile picture

Carlo Sferrazza

2,517 subscribers

24,389 views • 2 years ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

From simulation to reality 🤖 Robotics creator Skyentific built a walking bipedal robot using a simulation-first approach to design, test, and iterating in virtual environments before deploying in the real world. Powered by the NVIDIA Isaac platform and NVIDIA Jetson for on-device AI and control. 📖 #NationalRoboticsWeek
0:12

From simulation to reality 🤖 Robotics creator Skyentific built a walking bipedal robot using a simulation-first approach to design, test, and iterating in virtual environments before deploying in the real world. Powered by the NVIDIA Isaac platform and NVIDIA Jetson for on-device AI and control. 📖 #NationalRoboticsWeek

NVIDIA Robotics

21,868 views • 6 months ago

Using monocular slam + ego video to create usable reinforcement learning environments for embodied ai
0:14

Using monocular slam + ego video to create usable reinforcement learning environments for embodied ai

Chris Paxton

12,943 views • 1 month ago

A new Science Robotics study highlights a reinforcement learning-based, zero-shot approach that uses motion capture and animation to train humanoid and four-legged robots to perform acrobatics.
0:12

A new Science Robotics study highlights a reinforcement learning-based, zero-shot approach that uses motion capture and animation to train humanoid and four-legged robots to perform acrobatics.

Science Magazine

56,242 views • 1 month ago

The laws of physics apply everywhere. ⚛️ Co-developed by NVIDIA, Google DeepMind and Disney Research, Walt Disney Imagineering, the new Newton Beta, managed by the The Linux Foundation, now also runs with Isaac Lab and with MuJoCo Warp, which delivers warp speed for robot learning. Learn how to train a quadruped and simulate multiphysics in our tech blog. 🔗 #CoRL2025
0:11

The laws of physics apply everywhere. ⚛️ Co-developed by NVIDIA, Google DeepMind and Disney Research, Walt Disney Imagineering, the new Newton Beta, managed by the The Linux Foundation, now also runs with Isaac Lab and with MuJoCo Warp, which delivers warp speed for robot learning. Learn how to train a quadruped and simulate multiphysics in our tech blog. 🔗 #CoRL2025

NVIDIA Robotics

83,235 views • 1 year ago

What you’re seeing isn’t science fiction, it’s the future of robotics being trained in real time MasterBOT’s simulation engine accelerates AI learning without hardware, powered by community and data Welcome to the new era of Robotics x web3 $BOT
0:19

What you’re seeing isn’t science fiction, it’s the future of robotics being trained in real time MasterBOT’s simulation engine accelerates AI learning without hardware, powered by community and data Welcome to the new era of Robotics x web3 $BOT

MasterBOT

23,183 views • 1 year ago

Generalist robots need multimodal learning—fusing vision, touch, and body motion to handle unstructured worlds. 🦾 NVIDIA Isaac Lab is a GPU-accelerated, open-source simulation framework for robot learning that unifies physics, rendering, sensing, and parallel computing to train robot policies at scale. Learn more 🔗 #NVIDIAResearch
0:11

Generalist robots need multimodal learning—fusing vision, touch, and body motion to handle unstructured worlds. 🦾 NVIDIA Isaac Lab is a GPU-accelerated, open-source simulation framework for robot learning that unifies physics, rendering, sensing, and parallel computing to train robot policies at scale. Learn more 🔗 #NVIDIAResearch

NVIDIA Robotics

18,568 views • 8 months ago

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.
0:14

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

23,540 views • 1 year ago

RL X-mas came early. 🎄 For too long, building powerful AI agents with Reinforcement Learning has been blocked by GPU scarcity and complex infrastructure. That ends today. Introducing Serverless RL from wandb, powered by CoreWeave! We're making RL accessible to all.
0:15

RL X-mas came early. 🎄 For too long, building powerful AI agents with Reinforcement Learning has been blocked by GPU scarcity and complex infrastructure. That ends today. Introducing Serverless RL from wandb, powered by CoreWeave! We're making RL accessible to all.

Weights & Biases

112,774 views • 1 year ago

Holy moly, another big one by Google DeepMind! "Our SIMA 2 research offers a strong path towards applications in robotics and another step towards AGI in the real world" Google DeepMind just unveiled SIMA 2 - an AI agent that doesn’t just follow instructions but thinks and reasons with you. The first version, SIMA 1, learned to perform over 600 basic actions in 3D environments. SIMA 2 takes a leap forward: it understands goals, holds conversations, and learns from experience. This marks a shift from “Do this” to “Let’s figure out how to achieve this together.” A thread with examples and how it works🧵:
0:10

Holy moly, another big one by Google DeepMind! "Our SIMA 2 research offers a strong path towards applications in robotics and another step towards AGI in the real world" Google DeepMind just unveiled SIMA 2 - an AI agent that doesn’t just follow instructions but thinks and reasons with you. The first version, SIMA 1, learned to perform over 600 basic actions in 3D environments. SIMA 2 takes a leap forward: it understands goals, holds conversations, and learns from experience. This marks a shift from “Do this” to “Let’s figure out how to achieve this together.” A thread with examples and how it works🧵:

Chubby♨️

43,433 views • 11 months ago

We built a high-fidelity, cable-driven robotic hand in MuJoCo — accurately reproducing the full internal tendon system 🤖🧩 It’s now officially part of the MuJoCo Menagerie. On top of it, we developed a zero-shot RL training & real-world deployment pipeline based on MuJoCo Playground. Here’s a demo of Z-axis rotation, powered by tendon actuation ⚙️👇 💻 GitHub: 🔗 Learn more: #robotics #AI #reinforcementlearning #mujoco #opensource #sim2real #dexteroushand
0:10

We built a high-fidelity, cable-driven robotic hand in MuJoCo — accurately reproducing the full internal tendon system 🤖🧩 It’s now officially part of the MuJoCo Menagerie. On top of it, we developed a zero-shot RL training & real-world deployment pipeline based on MuJoCo Playground. Here’s a demo of Z-axis rotation, powered by tendon actuation ⚙️👇 💻 GitHub: 🔗 Learn more: #robotics #AI #reinforcementlearning #mujoco #opensource #sim2real #dexteroushand

Chestnut Robotics

10,866 views • 11 months ago

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at hello@qualiastudios.dev More soon
0:16

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soon

Qualia

88,190 views • 4 months ago

F.03 can now walk up/down stairs purely using it's onboard camera perception Our robots now walk from manufacturing when built to HQ This is trained end-to-end with reinforcement learning in simulation
0:14

F.03 can now walk up/down stairs purely using it's onboard camera perception Our robots now walk from manufacturing when built to HQ This is trained end-to-end with reinforcement learning in simulation

Brett Adcock

179,814 views • 6 months ago

Yeah! Teleoperation on low-cost robot arm from Tau Robotics working natively at 200Hz with and a very simple code. We are working a video tutorial to make it easy for anyone to reproduce our setup (in simulation or with a real robot). Stay tuned 😋
0:17

Yeah! Teleoperation on low-cost robot arm from Tau Robotics working natively at 200Hz with and a very simple code. We are working a video tutorial to make it easy for anyone to reproduce our setup (in simulation or with a real robot). Stay tuned 😋

Remi Cadene

31,512 views • 2 years ago

A new Science Robotics study highlights a reinforcement learning–based framework that integrates perceptual uncertainty into policy learning to equip humanoid robots with reactive soccer skills and dynamic behaviors.
0:19

A new Science Robotics study highlights a reinforcement learning–based framework that integrates perceptual uncertainty into policy learning to equip humanoid robots with reactive soccer skills and dynamic behaviors.

Science Magazine

56,716 views • 2 months ago

According to Demis Hassabis (Google DeepMind founder), "Continual Learning" from experience is a major missing capability for achieving AGI.
0:19

According to Demis Hassabis (Google DeepMind founder), "Continual Learning" from experience is a major missing capability for achieving AGI.

Rohan Paul

19,545 views • 9 months ago

I spend a lot of time finding out what VLMs still can't do pretty excited to help change that we launched Roboflow Labs at Roboflow training data, evals and environments for real-world tasks. vision, multimodal reasoning, robotics and coding. link:
0:13

I spend a lot of time finding out what VLMs still can't do pretty excited to help change that we launched Roboflow Labs at Roboflow training data, evals and environments for real-world tasks. vision, multimodal reasoning, robotics and coding. link:

SkalskiP

28,749 views • 1 month ago

The MuJoCo team just released a touch sensor using the new plugin API. It essentially aggregates all contact forces into an array of rectangular pixels to create an image. Here's a braille demo from the amazing Yuval Tassa:
0:11

The MuJoCo team just released a touch sensor using the new plugin API. It essentially aggregates all contact forces into an array of rectangular pixels to create an image. Here's a braille demo from the amazing Yuval Tassa:

Kevin Zakka

75,927 views • 3 years ago

Robot paper of the day: RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning UCL + Google DeepMind + Intrinsic built an AI planner that choreographs teams of arms to work in tight spaces without collisions—planning in seconds, not days.
0:10

Robot paper of the day: RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning UCL + Google DeepMind + Intrinsic built an AI planner that choreographs teams of arms to work in tight spaces without collisions—planning in seconds, not days.

Jiafei Duan

15,084 views • 1 year ago

1. Download Google AI Edge Gallery Access the official Google AI Edge Gallery GitHub repo. Go to the "Releases" section, then download and install the .apk file (Android). The iOS version is coming soon. Link:
0:11

1. Download Google AI Edge Gallery Access the official Google AI Edge Gallery GitHub repo. Go to the "Releases" section, then download and install the .apk file (Android). The iOS version is coming soon. Link:

Paul Couvert

54,452 views • 1 year ago