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Introducing LocoTouch: Quadrupedal robots equipped with tactile sensing can now transport unsecured objects — no mounts, no straps. The tactile policy transfers zero-shot from sim to real. Core Task-Agnostic Features: 1. High-fidelity contact simulation for distributed tactile sensors 2. Adaptive gait reward for symmetric, frequency-adaptive locomotion Website: Paper:

Changyi Lin

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🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.

🤖ROBOTS ARE GETTING SMARTER AT TOUCHING THE REAL WORLD Researchers from UC Berkeley, NVIDIA and Stanford introduced T-Rex, a framework that combines vision, language and tactile sensing. Instead of relying on cameras alone, robots can now respond to physical contact in real time. Robots are no longer just seeing objects. They’re learning how to FEEL them.

Coin Bureau

28,275 görüntüleme • 2 ay önce

1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n

1/🧠Humans are the best robot data source — but video alone misses one thing: force. 2/🙁Tactile gloves capture force — but they're costly and block the real touch manipulation depends on. 3/💪Maybe the future of touch lives on your wrist: surface EMG reads the muscles that cause force — tactile sensing without ever touching a tactile sensor. 4/🔥Want a fully open-source framework — hardware + software — to train your own force-aware learn-from-human-data robot policy? 🚀We introduce ForceBand: Learning Forceful Manipulation with sEMG -- bring force into human videos with sEMG, for force-aware manipulation ⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Force Beyond Vision ✦ Free-Hand Force Sensing ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Low-Cost & Easy-to-Implement Let's squeeze every bit of signal out of human data, and let robots feel the force! 🌐 Website: 📄 Paper: 💻 Code: 🎥 Video: 🧵 1/n

Zhi (Leo) Wang

52,211 görüntüleme • 2 ay önce

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

Who can say no to a humanoid robot that can actually feel a hug? 🤖 JQ Industries just built a fabric-based electronic skin for Unitree G1 — covering the torso, hands, and feet. This is not about appearance. It’s about giving robots real tactile sensing and pressure feedback. According to the company, the system includes tactile gloves, upper-body skin, and foot sensors. That means the robot can feel contact through its hands, detect pressure across its body, and use foot feedback to adjust balance. This is the missing layer in most humanoids. Vision alone can’t handle physical interaction. Robots need touch to grip safely, react to humans, and stay stable in real-world environments. JQ says its fiber sensors can detect forces as low as 0.01N, handle up to 3500kPa, and bend below 0.1mm radius. The system can also integrate with Isaac Sim, making tactile data usable for training and deployment. Unitree G1 is already built for manipulation and control — this adds the sensing layer it lacked. JQ says it’s now working with multiple humanoid companies, with some systems already shipping at scale. This is what happens when robots stop just seeing the world — and start physically feeling it.

RoboHub🤖

10,952 görüntüleme • 5 ay önce

No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.

No one could turn down a humanoid robot with human-like tactile sensitivity. JQ Industries equipped the Unitree G1 humanoid robot with a fabric-based electronic skin (piezoresistive flexible tactile sensors). A light touch can convert pressure, deformation, and slip into electrical signals, which are then processed by AI for real-time closed-loop control.(Integrates NVIDIA Isaac Sim) With this skin,humanoid robots start to behave more naturally: Touch sensing is no longer limited to dexterous hands,the entire body can feel, allowing the robot to stop or adjust posture instantly during collisions. It can also detect hugs, taps on the shoulder, or gentle touches, and respond in a softer, more human-like way. This matters especially in elder care and medical rehabilitation. A humanoid robot could gently help someone stand up, or control force precisely during assisted massage without harming the skin. Rigid mechanical systems deliver superhuman physical strength, while flexible sensing systems enable nuanced emotions. Electronic skin bridges high intelligence and high emotional intelligence.

CyberRobo

22,118 görüntüleme • 5 ay önce

A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:

A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable ⬇️ + Paper Grasping random objects is hard for robots, especially when shapes, weights, and materials vary. RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes. Thank you for sharing, Hui Zhang 🙏 Follow him!! What makes it special ✅ Grabs 500+ unseen objects with 94.6% success using only single-view input ✅ Learns local shapes, not full geometry, for better generalization ✅ Trained with just 35 objects in sim but works in the real world with hundreds more ✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning It shows that smart sensing and adaptive control can take dexterous grasping to the next level. Project: Paper:

Ilir Aliu

37,980 görüntüleme • 1 yıl önce

The sense of touch is the most criminally under-explored modality in robotics. Imagine doing sleight of hand wearing thick oven mitts. That's exactly how a robot feels today if it were alive. A magnetic piece snapping into place, a paper cup peeling out of a stack, a USB negotiating its way into the port - all invisible to the camera. Learning how to feel must be a full-stack co-designed effort. We are open-sourcing a principled methodology called "T-Rex": 1. Tactile as first-class citizen of the model. Our mixture-of-transformer runs two clocks asynchronously: a slow visuomotor expert plans the motion, and a fast tactile expert refines it in real time with high-frequency corrections at 4 "touch ticks" per vision tick. Forces change faster than frames arrive, so the architecture had to as well. 2. Open data. The largest tactile dataset ever released to our knowledge: a 50-hour (~5,500 episodes) high-quality, carefully synchronized robot play corpus, collected on SOTA tactile hand hardware with 22 degrees of freedom. Available today on HuggingFace! 3. Training recipe: T-Rex extends our prior work, EgoScale. Human egocentric videos for pretraining, a diverse dose of tactile robot play for mid-training. Our experiments show this bridges contact-free pretraining to contact-rich manipulation remarkably well. Pixels are cheap and everywhere, but they run out of steam at the moment of contact. Tactile will carry the last mile. The next scaling curve will be measured in hours of touch. T-Rex is a great collaboration between NVIDIA and Berkeley: 🧵

The sense of touch is the most criminally under-explored modality in robotics. Imagine doing sleight of hand wearing thick oven mitts. That's exactly how a robot feels today if it were alive. A magnetic piece snapping into place, a paper cup peeling out of a stack, a USB negotiating its way into the port - all invisible to the camera. Learning how to feel must be a full-stack co-designed effort. We are open-sourcing a principled methodology called "T-Rex": 1. Tactile as first-class citizen of the model. Our mixture-of-transformer runs two clocks asynchronously: a slow visuomotor expert plans the motion, and a fast tactile expert refines it in real time with high-frequency corrections at 4 "touch ticks" per vision tick. Forces change faster than frames arrive, so the architecture had to as well. 2. Open data. The largest tactile dataset ever released to our knowledge: a 50-hour (~5,500 episodes) high-quality, carefully synchronized robot play corpus, collected on SOTA tactile hand hardware with 22 degrees of freedom. Available today on HuggingFace! 3. Training recipe: T-Rex extends our prior work, EgoScale. Human egocentric videos for pretraining, a diverse dose of tactile robot play for mid-training. Our experiments show this bridges contact-free pretraining to contact-rich manipulation remarkably well. Pixels are cheap and everywhere, but they run out of steam at the moment of contact. Tactile will carry the last mile. The next scaling curve will be measured in hours of touch. T-Rex is a great collaboration between NVIDIA and Berkeley: 🧵

Jim Fan

172,307 görüntüleme • 27 gün önce

Reward AI just came out of stealth and unveiled OM-1, a robot policy learned entirely from humans. No teleoperation, no on-robot data. One Model, Any Body. 1. Capture. A wearable 7-DoF hand (Omnibody Hand) lets people demonstrate manipulation naturally while working, cooking, or sorting. Sensors record tactile, proximity, force and hand pose at full human speed. 2. Learn. OM-1 trains on this human data alone. No teleop, no on-robot experience. Then it runs across bodies from industrial arms to humanoids, and scaling is just adding more human data. 3. Control. A high-frequency control layer, trained with RL in sim, executes those actions on any robot, holding steady through unknown loads like a stuck fridge door. The result: OM-1 picks up a brand-new task from under 30 minutes of data.

The Humanoid Hub

34,345 görüntüleme • 3 gün önce

Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)

Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)

Abhishek Gupta

10,803 görüntüleme • 1 yıl önce

Excited to share the project that has surprised me the most in the last year! Large-scale RL in simulation, no demos and no reward engineering can solve dynamic, dexterous and contact rich tasks. The learned behaviors are reactive, forceful and use the environment for recovery in ways that are extremely challenging to bake in or teleoperate! You can play with the policies yourself to see: And, the learned behavior transfers to real world robots from RGB camera inputs! So what’s the trick - using simulator resets carefully! Let’s unpack (1/10)

Excited to share the project that has surprised me the most in the last year! Large-scale RL in simulation, no demos and no reward engineering can solve dynamic, dexterous and contact rich tasks. The learned behaviors are reactive, forceful and use the environment for recovery in ways that are extremely challenging to bake in or teleoperate! You can play with the policies yourself to see: And, the learned behavior transfers to real world robots from RGB camera inputs! So what’s the trick - using simulator resets carefully! Let’s unpack (1/10)

Abhishek Gupta

83,058 görüntüleme • 5 ay önce

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:

A policy that teaches robot hands to touch things the way humans do... not just grab and move, but feel and adjust in real time. Robot manipulation research often stops at picking up objects and placing them. CGP goes further: it handles tasks like opening jars, flipping objects in-hand, wiping dishes, and grasping fragile eggs, the kind of dexterous, contact-rich skills that require constant micro-adjustments based on what the fingers are actually feeling. The robot doesn't just see what it's doing; it predicts what contact should feel like at each step, then checks whether reality matches the prediction. If a finger is slipping, the policy knows before the object drops. Works on real robot hands (both 4-finger and 5-finger designs) with tactile sensors embedded in the fingertips Robust to visual distractions! The robot keeps flipping a box correctly even when the camera view is disrupted, because it's grounding decisions in touch, not just vision. Baseline policies without contact grounding fail in predictable ways: slipping mid-task, incomplete motions, loss of grasp, CGP avoids these This is a meaningful step toward robots that can handle the physical world with the kind of reliable, adaptive grip that humans take for granted. Relevant for manufacturing, logistics, assistive robotics, and anywhere fragile or irregular objects need to be handled carefully. Published at RSS 2026, developed with Meta Reality Labs Research. Thanks for sharing, Zhengtong Xu / Zhengtong Xu ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,769 görüntüleme • 3 ay önce

A robot that sees the terrain and predicts its own future… up to 5 seconds ahead? This is real. ❗️Best Systems Paper finalist at #RSS2025 The team introduces a perceptive Forward Dynamics Model that helps legged robots safely navigate rough, complex environments: no manual tuning no rigid-body limits. Why this matters ✅ Predicts future robot states using both perception and proprioception ✅ Trained on years of sim + real data for strong sim-to-real performance ✅ Enables zero-shot navigation with minimal cost tuning ✅ Boosts success rates and safety on rough terrain like never before This isn’t just another planner, it’s a big step toward robust, adaptable locomotion. Paper: Project website:

A robot that sees the terrain and predicts its own future… up to 5 seconds ahead? This is real. ❗️Best Systems Paper finalist at #RSS2025 The team introduces a perceptive Forward Dynamics Model that helps legged robots safely navigate rough, complex environments: no manual tuning no rigid-body limits. Why this matters ✅ Predicts future robot states using both perception and proprioception ✅ Trained on years of sim + real data for strong sim-to-real performance ✅ Enables zero-shot navigation with minimal cost tuning ✅ Boosts success rates and safety on rough terrain like never before This isn’t just another planner, it’s a big step toward robust, adaptable locomotion. Paper: Project website:

Ilir Aliu

43,990 görüntüleme • 1 yıl önce

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

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 görüntüleme • 8 ay önce

Humanoids are finally getting a sense of touch that actually feels... human. 🤖 As third-gen bots like Optimus, XPENG, and Figure move from labs to the real world, the focus is shifting from just "walking" to "feeling." We are seeing a move toward tactile fabrics that can be literally tailored to a robot's frame like a custom suit. This isn't just about simple pressure sensors; it's about a full electronic skin with a folding radius under 0.2mm. That means a bot can detect a gram-level touch, sense textures, or even feel the slight slip of an object before it drops. The industrial impact is massive—the global market for these flexible sensors is projected to hit 27.4 billion RMB (approx. $3.98B) by 2030. Beyond just picking up boxes, this tech allows robots to sense the warmth and pressure of a hug or adjust balance through foot sensors. High-density arrays with dozens of sensors per square centimeter are becoming the new baseline for dexterity. By turning physical contact into high-fidelity data, these skins are the final key to making robots safe for our homes and hospitals. The future of robotics is looking a lot more flexible and a lot more sensitive.

Humanoids are finally getting a sense of touch that actually feels... human. 🤖 As third-gen bots like Optimus, XPENG, and Figure move from labs to the real world, the focus is shifting from just "walking" to "feeling." We are seeing a move toward tactile fabrics that can be literally tailored to a robot's frame like a custom suit. This isn't just about simple pressure sensors; it's about a full electronic skin with a folding radius under 0.2mm. That means a bot can detect a gram-level touch, sense textures, or even feel the slight slip of an object before it drops. The industrial impact is massive—the global market for these flexible sensors is projected to hit 27.4 billion RMB (approx. $3.98B) by 2030. Beyond just picking up boxes, this tech allows robots to sense the warmth and pressure of a hug or adjust balance through foot sensors. High-density arrays with dozens of sensors per square centimeter are becoming the new baseline for dexterity. By turning physical contact into high-fidelity data, these skins are the final key to making robots safe for our homes and hospitals. The future of robotics is looking a lot more flexible and a lot more sensitive.

RoboHub🤖

72,367 görüntüleme • 5 ay önce

U.S. based humanoid robotics company 1X has just unveiled their new tendon-driven robot hands with 25 degrees of freedom (DOF). • Made in USA • Tendon Drive Ratio: 5:1–15:1 • Wrist Dexterity: 3 DOF • Backdrivability: Fully backdrivable • Tactile Sensing: Pressure + location + slip • Finger Force: Up to 45 N • Wrist Torque: 17.75 Nm • Position Accuracy: ±0.2 mm • Waterproof Rating: IP68 • Reliability: >2 million cycles "These hands are designed to do something fundamental: remove the hardware ceiling on what humanoid robots can actually do, and make data the only barrier to capabilities. By matching or surpassing human hands across the dimensions that matter, they ensure our AI models are no longer limited by dexterity. NEO can now perform virtually any task a human can do with their hands– with the precision, adaptability, and gentleness required for real-world environments."

U.S. based humanoid robotics company 1X has just unveiled their new tendon-driven robot hands with 25 degrees of freedom (DOF). • Made in USA • Tendon Drive Ratio: 5:1–15:1 • Wrist Dexterity: 3 DOF • Backdrivability: Fully backdrivable • Tactile Sensing: Pressure + location + slip • Finger Force: Up to 45 N • Wrist Torque: 17.75 Nm • Position Accuracy: ±0.2 mm • Waterproof Rating: IP68 • Reliability: >2 million cycles "These hands are designed to do something fundamental: remove the hardware ceiling on what humanoid robots can actually do, and make data the only barrier to capabilities. By matching or surpassing human hands across the dimensions that matter, they ensure our AI models are no longer limited by dexterity. NEO can now perform virtually any task a human can do with their hands– with the precision, adaptability, and gentleness required for real-world environments."

Sawyer Merritt

744,530 görüntüleme • 2 ay önce

Excited to introduce SOLE-R1, a video-language reasoning model for zero-shot reward prediction for robot manipulation tasks! SOLE-R1 reasoning can serve as the SOLE signal for learning new tasks (completely from scratch) through online RL - i.e., robots start with random actions and learn previously unseen tasks guided only by SOLE-R1 rewards, without any demonstrations, ground-truth rewards, success indicators, or task-specific tuning. SOLE-R1 significantly outperforms strong baselines (e.g., Robometer, RoboReward, TOPReward, GPT-5, Gemini-3-Pro) in zero-shot online RL when evaluated across 40 tasks - including a real-world tabletop manipulation setting and 4 sim environments (LIBERO, ManiSkill, Meta-World, RoboSuite). We open source all models, training data, and code. Website, demos, and paper at: 🧵 (1/6)

Excited to introduce SOLE-R1, a video-language reasoning model for zero-shot reward prediction for robot manipulation tasks! SOLE-R1 reasoning can serve as the SOLE signal for learning new tasks (completely from scratch) through online RL - i.e., robots start with random actions and learn previously unseen tasks guided only by SOLE-R1 rewards, without any demonstrations, ground-truth rewards, success indicators, or task-specific tuning. SOLE-R1 significantly outperforms strong baselines (e.g., Robometer, RoboReward, TOPReward, GPT-5, Gemini-3-Pro) in zero-shot online RL when evaluated across 40 tasks - including a real-world tabletop manipulation setting and 4 sim environments (LIBERO, ManiSkill, Meta-World, RoboSuite). We open source all models, training data, and code. Website, demos, and paper at: 🧵 (1/6)

Philip Schroeder

19,275 görüntüleme • 3 ay önce