Загрузка видео...

Не удалось загрузить видео

На главную

Researchers at MIT developed Draft, a tool that lets engineers design and customize robots by adjusting parameters such as limb length, body size, and motor power. It uses real-world hardware data and reinforcement learning to estimate robot feasibility and test performance in simulation. The tool helps researchers explore robot...

spaceandtech_'s profile picture

Space and Technology

42,228 subscribers

29,566 просмотров • 1 месяц назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 2

Фото профиля Sai Kishor Kothakota
Sai Kishor Kothakota29 дней назад

Can you share the link to the tool? I'm interested to explore

Фото профиля clankr
clankr1 месяц назад

Cool stuff, also there was a transformer based robot designer project similar to this

Похожие видео

Researchers have developed Humanoid-GPT, a new AI system that helps humanoid robots perform complex movements and tasks. Tested on the Unitree G1 robot, it enables real-time whole-body control and can perform new actions without special training. During demonstrations, the robot successfully carried out a variety of movements and tasks, showing strong potential for real-world applications.
0:28

Researchers have developed Humanoid-GPT, a new AI system that helps humanoid robots perform complex movements and tasks. Tested on the Unitree G1 robot, it enables real-time whole-body control and can perform new actions without special training. During demonstrations, the robot successfully carried out a variety of movements and tasks, showing strong potential for real-world applications.

Space and Technology

22,330 просмотров • 4 месяцев назад

RoboBall is a spherical robot developed by researchers at Texas A&M University to explore lunar craters. It moves using a pendulum system inside the ball, allowing it to roll and steer in any direction. The robot can reach speeds of up to 20 mph, and researchers are working to make it faster and more autonomous for future space missions.
0:34

RoboBall is a spherical robot developed by researchers at Texas A&M University to explore lunar craters. It moves using a pendulum system inside the ball, allowing it to roll and steer in any direction. The robot can reach speeds of up to 20 mph, and researchers are working to make it faster and more autonomous for future space missions.

Space and Technology

20,859 просмотров • 5 месяцев назад

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.
0:58

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

The Humanoid Hub

46,715 просмотров • 1 год назад

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla
1:06:07

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

The TWIML AI Podcast

22,592 просмотров • 10 месяцев назад

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!
53:45

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

38,867 просмотров • 5 месяцев назад

Researchers at EPFL in Switzerland have developed a bioinspired robot called GOAT that can change its shape to adapt to different environments. It moves by adjusting the length of its cables, allowing it to morph and travel efficiently across various terrains. This shape-shifting ability makes it useful for exploration and rescue missions, and it was introduced in 2025.
0:29

Researchers at EPFL in Switzerland have developed a bioinspired robot called GOAT that can change its shape to adapt to different environments. It moves by adjusting the length of its cables, allowing it to morph and travel efficiently across various terrains. This shape-shifting ability makes it useful for exploration and rescue missions, and it was introduced in 2025.

Space and Technology

14,170 просмотров • 7 месяцев назад

A robot snake for the rescue! 🛟 Mechanum wheels, reconfigurable shape, camera set. These robots were developed by researchers at The University of Manchester. This machine is called MIRRAX-150, and it performs inspections as well as explorations. To map 3D point clouds, this machine uses low-cost components, 3D printing, and LiDAR technology. 🧭 Soon, this robot will be deployed on the Sellafield nuclear power station. Basically, it's a snake that changes its body! P.S. What use cases would fit these robots? 🐍 ~~~ ♻️ RT to help 1 robot find a new workplace.
0:31

A robot snake for the rescue! 🛟 Mechanum wheels, reconfigurable shape, camera set. These robots were developed by researchers at The University of Manchester. This machine is called MIRRAX-150, and it performs inspections as well as explorations. To map 3D point clouds, this machine uses low-cost components, 3D printing, and LiDAR technology. 🧭 Soon, this robot will be deployed on the Sellafield nuclear power station. Basically, it's a snake that changes its body! P.S. What use cases would fit these robots? 🐍 ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

15,263 просмотров • 1 год назад

iRonCub3 is the world’s first jet-powered humanoid robot developed by researchers at the Italian Institute of Technology. It uses four jet engines that allow it to fly and handle complex missions. The robot is designed for extreme conditions and could help in disaster relief by reaching hard-to-access areas.
0:30

iRonCub3 is the world’s first jet-powered humanoid robot developed by researchers at the Italian Institute of Technology. It uses four jet engines that allow it to fly and handle complex missions. The robot is designed for extreme conditions and could help in disaster relief by reaching hard-to-access areas.

Space and Technology

23,668 просмотров • 6 месяцев назад

The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.
1:01

The coolest part of Microduck might not be the robot. It's how it learns to move. This repo shows the full reinforcement learning pipeline behind Microduck. The robot learns in simulation using MuJoCo, PPO and GPU-based training, then the trained policy is exported to ONNX and deployed on the real robot. But the real challenge is sim-to-real. The simulation models things like actuator behavior, battery voltage, command delays, friction and even gear backlash. That helps make the learned policy work when it moves from simulation to the physical robot. The repo includes policies for walking, fall recovery, sitting, standing, kicking, ground picking and even roller skating. Train in simulation. Test the behavior. Export the policy. Run it on a real robot.

Praveen Kumar Verma

140,498 просмотров • 2 месяцев назад

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
0:29

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,864 просмотров • 9 месяцев назад

A team of researchers at EPFL's Reconfigurable Robotics Lab developed an origami-like robot that can change shape, move, and interact with objects and people.
0:47

A team of researchers at EPFL's Reconfigurable Robotics Lab developed an origami-like robot that can change shape, move, and interact with objects and people.

TechCrunch

906,932 просмотров • 2 лет назад

Training fully autonomous robots takes more data than the real world can easily provide. Simulation lets us train, test, and deploy autonomous software without being limited by robots, space, or cost. Here’s OM1 running in simulation side-by-side to the real world.
1:26

Training fully autonomous robots takes more data than the real world can easily provide. Simulation lets us train, test, and deploy autonomous software without being limited by robots, space, or cost. Here’s OM1 running in simulation side-by-side to the real world.

OpenMind

121,631 просмотров • 11 месяцев назад

Researchers developed Hitter, a humanoid robot that can play table tennis in real-world settings. It was trained on the Unitree G1 robot to predict the ball’s path, choose the right shot, and react in less than a second. In tests, it achieved 106 consecutive shots with a human and even rallied with another humanoid robot.
0:30

Researchers developed Hitter, a humanoid robot that can play table tennis in real-world settings. It was trained on the Unitree G1 robot to predict the ball’s path, choose the right shot, and react in less than a second. In tests, it achieved 106 consecutive shots with a human and even rallied with another humanoid robot.

Space and Technology

25,736 просмотров • 7 месяцев назад

The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.
0:32

The quadruped you can assemble yourself. Stella is an educational quadruped robot developed by Ahead. Designed for hands-on learning and self-assembly, the robot uses durable 3D-printed components. Ideal for all education levels, it is easy to repair, infinitely extendable, and equipped with advanced hardware and software. Despite its compact design, Stella is versatile, capable of operating in various environments and even carrying payloads like an optional robotic arm. A powerful tool for exploring robotics.

Circuit

23,262 просмотров • 1 год назад

We're hiring in robot learning at World Labs! Join me, Fei-Fei Li, and the team to define and scale the next generation of world models for robot learning! Atlas for Robotics: Real-to-Sim-to-Real: Apply:
0:41

We're hiring in robot learning at World Labs! Join me, Fei-Fei Li, and the team to define and scale the next generation of world models for robot learning! Atlas for Robotics: Real-to-Sim-to-Real: Apply:

Yunzhu Li

177,209 просмотров • 1 месяц назад

Today we are launching 100 early pre-orders for Mr. Tensors eyecandy robotics, an inexpensive opensource lifelike robot character that's cool enough toy for kids (and a human-robot-interaction + reinforcement learning research tool for academia and hobbyists). We are looking for parents and researchers to adopt our handmade cute little dinos! The early 100 people would more or less be our design partners and early supporters to help us manufacture, ship and iterate on the product experience.
1:32

Today we are launching 100 early pre-orders for Mr. Tensors eyecandy robotics, an inexpensive opensource lifelike robot character that's cool enough toy for kids (and a human-robot-interaction + reinforcement learning research tool for academia and hobbyists). We are looking for parents and researchers to adopt our handmade cute little dinos! The early 100 people would more or less be our design partners and early supporters to help us manufacture, ship and iterate on the product experience.

mansin

118,467 просмотров • 1 год назад

RoboParty built a humanoid robot in 4 months. Now, the full project is open source. ROBOTO ORIGIN stands about 1.25 m tall, weighs 34 kg and has 23 degrees of freedom. The technical details: • 120 Nm peak torque at the leg joints • 48 V, 15 Ah battery • Mechanical CAD, PCB designs and BOM • Firmware, motor control and ROS 2 deployment • URDF/MJCF models for simulation • Reinforcement learning with Isaac Lab and RSL-RL • Sim-to-sim transfer to MuJoCo and sim-to-real validation The robot can walk, run and jump. What caught my attention is the training to hardware workflow. You can study how learned motion policies move from simulation to a physical humanoid.
1:25

RoboParty built a humanoid robot in 4 months. Now, the full project is open source. ROBOTO ORIGIN stands about 1.25 m tall, weighs 34 kg and has 23 degrees of freedom. The technical details: • 120 Nm peak torque at the leg joints • 48 V, 15 Ah battery • Mechanical CAD, PCB designs and BOM • Firmware, motor control and ROS 2 deployment • URDF/MJCF models for simulation • Reinforcement learning with Isaac Lab and RSL-RL • Sim-to-sim transfer to MuJoCo and sim-to-real validation The robot can walk, run and jump. What caught my attention is the training to hardware workflow. You can study how learned motion policies move from simulation to a physical humanoid.

Techniahqrobot | humanoid robots

12,329 просмотров • 16 дней назад

From text to reality: MIT researchers find new ways to use LLMs to help in design & manufacturing. These models can convert text prompts to CAD, generate manufacturing instructions, and search for optimal designs:
1:25

From text to reality: MIT researchers find new ways to use LLMs to help in design & manufacturing. These models can convert text prompts to CAD, generate manufacturing instructions, and search for optimal designs:

MIT CSAIL

59,161 просмотров • 3 лет назад