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Humanoid robots playing table tennis fully autonomously. The 'HITTER' system combines a model-based planner with a reinforcement learning (RL) whole-body controller. It is fully autonomous but relies on an external sensing system. A 9-camera OptiTrack motion capture setup tracks the ball, which is covered with reflective markers, to predict... show more

The Humanoid Hub

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91,980 Aufrufe • vor 1 Jahr •via X (Twitter)

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21 Kommentare

Profilbild von The Humanoid Hub

The Humanoid Hubvor 1 Jahr

This Google DeepMind work, published last year, used 22 cameras mounted in the play area to track the ball and the opponent's paddle, providing real-time data to its hierarchical policy.

Profilbild von Hic Rhodus Hic Salta

Hic Rhodus Hic Saltavor 1 Jahr

Robots will have instant access to the hive mind. They can quickly receive detailed information from any nearby source. The capacities of humanoids and robots will be uncanny.

Profilbild von Techraan

Techraanvor 1 Jahr

okay okay okay... Even though it needs external sensors, this is still incredible.

Profilbild von Koala

Koalavor 1 Jahr

Wow super cool to see this in a humanoid robot

Profilbild von Himanshu Kumar

Himanshu Kumarvor 1 Jahr

Impressive integration of planning and RL. External sensing remains a constraint to broader application though. Where could this lead us next?

Profilbild von Avani Rajput

Avani Rajputvor 1 Jahr

Watching humanoid robots play table tennis like this is insane — the precision and agility are mind-blowing.

Profilbild von Fabien Musty

Fabien Mustyvor 1 Jahr

If it relies on external sensors and precise conditions, it's more akin to ABB's industrial sorting robot.

Profilbild von FintechDoc

FintechDocvor 1 Jahr

This video would have been science fiction a few years ago. Very impressive.

Profilbild von Let's learnrobot.com®

Let's learnrobot.com®vor 1 Jahr

🤖🏓

Profilbild von 💀Co-Founder, Skeleton Crew Inc.

💀Co-Founder, Skeleton Crew Inc.vor 1 Jahr

But not a single robot on hand to price the match and book action? Sad.

Profilbild von Let it ₿

Let it ₿vor 1 Jahr

For real??

Profilbild von Alexander B

Alexander Bvor 1 Jahr

Mother fuckers!

Profilbild von 好湿好湿啊

好湿好湿啊vor 1 Jahr

they are getting a lot faster

Profilbild von Crypto Ndo! 🇮🇩

Crypto Ndo! 🇮🇩vor 1 Jahr

Hey sir @elonmusk @_Shadow36

Profilbild von Biglow 🇫🇷🥖

Biglow 🇫🇷🥖vor 1 Jahr

@vision_ia

Profilbild von Dante

Dantevor 1 Jahr

@grok Can a robot with high-speed radar track a table tennis ball coated in radar-reflective material, with measures to reduce signal interference from nearby objects ?

Profilbild von Cameron Carter🌐

Cameron Carter🌐vor 1 Jahr

Imagine in 5 years what’s possible. I still don’t believe we hit the AI exponential yet!

Profilbild von Zoom manga

Zoom mangavor 1 Jahr

that’s so cool.

Profilbild von GHOSTROSIN

GHOSTROSINvor 1 Jahr

@Tesla_Optimus

Profilbild von Very Parts

Very Partsvor 1 Jahr

Aren't they using mo-cap to get robot and ball precise positions in this setup? Still very impressive!

Profilbild von Superman

Supermanvor 1 Jahr

That’s insane. Soon these crankers will replace all humans.

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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.

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 Aufrufe • vor 1 Jahr

Researchers developed a system called LATENT that teaches humanoid robots to play tennis using small pieces of human movement data. It learns basic motion fragments, then corrects and combines them to create natural movements and swings. The system was tested on the Unitree G1 humanoid robot, enabling it to hit balls and rally with human players.

Researchers developed a system called LATENT that teaches humanoid robots to play tennis using small pieces of human movement data. It learns basic motion fragments, then corrects and combines them to create natural movements and swings. The system was tested on the Unitree G1 humanoid robot, enabling it to hit balls and rally with human players.

Space and Technology

45,748 Aufrufe • vor 4 Monaten

NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

Léo

23,248 Aufrufe • vor 13 Tagen

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

Galbot

46,125 Aufrufe • vor 6 Monaten

🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗:

🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗:

Tairan He

96,950 Aufrufe • vor 2 Jahren

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

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Sawyer Merritt

624,910 Aufrufe • vor 8 Monaten

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The Humanoid Hub

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New release from Meta FAIR — Meta Motivo is a first-of-its-kind behavioral foundation model for controlling virtual physics-based humanoid agents for a wide range of complex whole-body tasks. The model is capable of expressing human-like behaviors and achieves performance competitive with task-specific methods and outperforms state-of-the-art unsupervised RL and model-based baselines. Try the demo ➡️ Get the model and code ➡️ We’re excited about how this research could pave the way for fully embodied agents, leading to more lifelike NPCs, democratization of character animation and new types of immersive experiences.

AI at Meta

129,283 Aufrufe • vor 1 Jahr

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

Ashish Kapoor

118,467 Aufrufe • vor 1 Jahr

HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

The Humanoid Hub

17,395 Aufrufe • vor 1 Jahr

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 Aufrufe • vor 7 Monaten

This is the Huawei Mate 90 Pro Max, paired with an external camera. It is important to emphasize that this is a true “external camera,” not a traditional external lens or telephoto attachment. The external camera itself features an independent 1-inch sensor, with up to 10x optical zoom and 100x digital zoom. In other words, from the sensor to the lens, it is a complete and independent imaging system in its own right. It is also very simple to use. Just attach it to the camera area of the Mate 90 Pro Max, and you can control the external camera directly through the phone’s screen, operating system, and camera software. In this setup, the phone’s own camera does not participate in image capture. The camera area mainly serves to secure and connect the external camera. The actual photography is handled by the external camera’s own 1-inch sensor and 24mm-to-240mm lens. This is also the biggest difference between it and traditional smartphone telephoto attachments. Traditional telephoto attachments still rely on the phone’s original camera to capture images, whereas this solution adds a complete, independent camera system to the phone. In a sense, you can think of the Mate 90 Pro Max as the external camera’s display, operating system, and computing platform. In theory, this external camera could also be used with other phones, as long as it is securely mounted and properly adapted through the appropriate connection method.

This is the Huawei Mate 90 Pro Max, paired with an external camera. It is important to emphasize that this is a true “external camera,” not a traditional external lens or telephoto attachment. The external camera itself features an independent 1-inch sensor, with up to 10x optical zoom and 100x digital zoom. In other words, from the sensor to the lens, it is a complete and independent imaging system in its own right. It is also very simple to use. Just attach it to the camera area of the Mate 90 Pro Max, and you can control the external camera directly through the phone’s screen, operating system, and camera software. In this setup, the phone’s own camera does not participate in image capture. The camera area mainly serves to secure and connect the external camera. The actual photography is handled by the external camera’s own 1-inch sensor and 24mm-to-240mm lens. This is also the biggest difference between it and traditional smartphone telephoto attachments. Traditional telephoto attachments still rely on the phone’s original camera to capture images, whereas this solution adds a complete, independent camera system to the phone. In a sense, you can think of the Mate 90 Pro Max as the external camera’s display, operating system, and computing platform. In theory, this external camera could also be used with other phones, as long as it is securely mounted and properly adapted through the appropriate connection method.

Ice Universe

58,967 Aufrufe • vor 3 Tagen

The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

gensyn

228,919 Aufrufe • vor 1 Jahr

Humanoid robots can now play tennis in the style of Nadal, Federer, and Djokovic, learned from broadcast footage. AdaPT (Noitom Robotics, Shanghai AI Lab, Dobot, SJTU) extracts professional player motions from TV broadcasts, retargets them to a humanoid, and learns both rallying and serving while preserving each player's distinctive style. The core problem: decoupled planning-and-tracking works in sim but degrades badly on real hardware. Their fix is speed adaptation, training the tracker on randomized execution speeds while the planner learns to adjust motion speed to the incoming ball. Deployed on Unitree G1 and the full-size Dobot Atom. Worth noting on the sensing: the robot has no onboard vision. Ball and robot position both come from external infrastructure, a 35-camera motion-capture arena in the lab. Paper:

Humanoid robots can now play tennis in the style of Nadal, Federer, and Djokovic, learned from broadcast footage. AdaPT (Noitom Robotics, Shanghai AI Lab, Dobot, SJTU) extracts professional player motions from TV broadcasts, retargets them to a humanoid, and learns both rallying and serving while preserving each player's distinctive style. The core problem: decoupled planning-and-tracking works in sim but degrades badly on real hardware. Their fix is speed adaptation, training the tracker on randomized execution speeds while the planner learns to adjust motion speed to the incoming ball. Deployed on Unitree G1 and the full-size Dobot Atom. Worth noting on the sensing: the robot has no onboard vision. Ball and robot position both come from external infrastructure, a 35-camera motion-capture arena in the lab. Paper:

The Humanoid Hub

86,367 Aufrufe • vor 1 Monat

Picking up laundry with a 3D printed robot arm and LeRobot. It operates using SO-ARM101 arm mounted on a mobile base, relying on just a wrist and base camera. The base is controlled with an Xbox controller by a human operator. The arm is fully autonomous thanks to the ACT model. The robot can generalize to previously unseen environments. The technical stack: - Model: Action Chunking Transformer (ACT) with Temporal Ensemble - Data: Learned via teleoperation including negative examples and failure recoveries - Framework: Built and trained locally using LeRobot on a single consumer GPU in 3.5h This is a perfect example that real-world robotics isn't about perfect lab conditions - it's about messy living rooms, confusing shadows, and learning to recover from mistakes. And with open tools, anyone can tackle these edge cases right from their desk.

Picking up laundry with a 3D printed robot arm and LeRobot. It operates using SO-ARM101 arm mounted on a mobile base, relying on just a wrist and base camera. The base is controlled with an Xbox controller by a human operator. The arm is fully autonomous thanks to the ACT model. The robot can generalize to previously unseen environments. The technical stack: - Model: Action Chunking Transformer (ACT) with Temporal Ensemble - Data: Learned via teleoperation including negative examples and failure recoveries - Framework: Built and trained locally using LeRobot on a single consumer GPU in 3.5h This is a perfect example that real-world robotics isn't about perfect lab conditions - it's about messy living rooms, confusing shadows, and learning to recover from mistakes. And with open tools, anyone can tackle these edge cases right from their desk.

LeRobot

69,148 Aufrufe • vor 1 Monat

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.

Dev Shah

13,730 Aufrufe • vor 1 Monat

The wait is over. Figure 03 is here, "Designed for Helix, the home, and the world at scale." ⦿ Shown testing at home, performing long-horizon tasks fully autonomously (nothing teleoperated). ⦿ A completely redesigned sensory suite and hand system, which is purpose-built to enable Helix, Figure's proprietary VLA AI. ⦿ 9% lighter than Figure 02. ⦿ Features 2kW wireless charging, an improved audio system for voice reasoning, and battery safety advancements. ⦿ Figure established a new supply chain and an entirely new process for manufacturing humanoid robots. ⦿ A new compact camera architecture delivers twice the frame rate, one-quarter the latency, and a 60% wider field of view per camera. ⦿ An embedded palm camera with a wide field of view and low-latency sensing. ⦿ First-generation in-house developed tactile sensors for fingertips, capable of detecting forces as small as three grams of pressure. ⦿ Includes 10 Gbps mmWave data offload capability. ⦿ Better protection against pinch points. Figure's manufacturing facility, BotQ, has a first-generation manufacturing line that will initially be capable of producing up to 12,000 humanoid robots per year, with the goal of producing a total of 100,000 robots over the next four years.

The wait is over. Figure 03 is here, "Designed for Helix, the home, and the world at scale." ⦿ Shown testing at home, performing long-horizon tasks fully autonomously (nothing teleoperated). ⦿ A completely redesigned sensory suite and hand system, which is purpose-built to enable Helix, Figure's proprietary VLA AI. ⦿ 9% lighter than Figure 02. ⦿ Features 2kW wireless charging, an improved audio system for voice reasoning, and battery safety advancements. ⦿ Figure established a new supply chain and an entirely new process for manufacturing humanoid robots. ⦿ A new compact camera architecture delivers twice the frame rate, one-quarter the latency, and a 60% wider field of view per camera. ⦿ An embedded palm camera with a wide field of view and low-latency sensing. ⦿ First-generation in-house developed tactile sensors for fingertips, capable of detecting forces as small as three grams of pressure. ⦿ Includes 10 Gbps mmWave data offload capability. ⦿ Better protection against pinch points. Figure's manufacturing facility, BotQ, has a first-generation manufacturing line that will initially be capable of producing up to 12,000 humanoid robots per year, with the goal of producing a total of 100,000 robots over the next four years.

The Humanoid Hub

169,159 Aufrufe • vor 11 Monaten

Tesla just got granted new patent for robotic Knee join and of course I have to make a video about it 😀 This patent application describes a sophisticated robotic knee joint designed by Tesla to emulate the complex movement of a human leg. The invention utilizes a linear actuator and a specialized linkage system to provide a wide range of motion while maintaining energy efficiency. By integrating processing circuitry and a controller, the system can precisely calculate and execute the necessary torque and displacement for fluid mobility. This mechanical design is specifically tailored for use in humanoid robots, allowing them to perform tasks with a more natural and effective gait. The documentation further includes technical illustrations and simulation data to demonstrate how the joint achieves optimal performance through its unique structural configuration.

Tesla just got granted new patent for robotic Knee join and of course I have to make a video about it 😀 This patent application describes a sophisticated robotic knee joint designed by Tesla to emulate the complex movement of a human leg. The invention utilizes a linear actuator and a specialized linkage system to provide a wide range of motion while maintaining energy efficiency. By integrating processing circuitry and a controller, the system can precisely calculate and execute the necessary torque and displacement for fluid mobility. This mechanical design is specifically tailored for use in humanoid robots, allowing them to perform tasks with a more natural and effective gait. The documentation further includes technical illustrations and simulation data to demonstrate how the joint achieves optimal performance through its unique structural configuration.

Emily

50,333 Aufrufe • vor 5 Monaten