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🚀 Introducing SoftMimic: Compliant humanoids for an interactive future — bringing humanoids into the real world 🤝 🔗 Current humanoids collapse when they touch the world — they can’t handle contact or deviation from their reference motion 😭 😎SoftMimic learns to deviate gracefully from motion references. It natively embraces...

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Pulkit Agrawal

17,530 subscribers

40,286 просмотров • 10 месяцев назад •via X (Twitter)

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Комментарии: 19

Фото профиля Pieter Abbeel
Pieter Abbeel10 месяцев назад

impressive

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

😍Unlike today's stiff humanoids, soft mimic is robust to unexpected collisions 🔗

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

🤩Using the same motion reference, soft mimic picks up boxes of different sizes -- free generalization! 🔗

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

🙀🤯Soft mimic is extremely robust while still being safe and doing what you want the robot to do. 🔗

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

🥳Soft mimic is compliant enabling gentle interactions 🔗

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

🤯🤩Soft mimic can be quickly programmed for new tasks, zero-shot 🔗

Фото профиля James
James10 месяцев назад

It has invisible lats syndrome

Фото профиля Mengdi Xu
Mengdi Xu10 месяцев назад

Very cool results. Congrats!

Фото профиля Sunil Gora
Sunil Gora10 месяцев назад

Impressive work! Isn't it learning the 'admittance control'?

Фото профиля Pulkit Agrawal
Pulkit Agrawal10 месяцев назад

yes

Фото профиля czr
czr10 месяцев назад

cool！

Фото профиля Sammy
Sammy10 месяцев назад

Amazing

Фото профиля ζ Pedram ζ
ζ Pedram ζ10 месяцев назад

Would love to see code on this, but this is the most practical way to real scalability. Also minimum requirements for Kung Fu

Фото профиля Kaival Shah 🤔
Kaival Shah 🤔10 месяцев назад

Super cool!

Фото профиля ζ Pedram ζ
ζ Pedram ζ10 месяцев назад

So nice

Фото профиля Michal Nauman
Michal Nauman10 месяцев назад

Awesome work 🦾

Фото профиля Carlos DP 🤖🇺🇸
Carlos DP 🤖🇺🇸10 месяцев назад

Very cool work! Similar to the goals of FALCON, but within a motion tracking context, I like it

Фото профиля Sarika saah🇺🇸
Sarika saah🇺🇸6 месяцев назад

In the real world, true success lies in not falling over from bumps and touches, and SoftMimic has nailed this point. How easy is it to deploy this framework on robots like Unitree or Digit? 😌

Фото профиля Priyanka Agrawal
Priyanka Agrawal9 месяцев назад

The future depends on robots.

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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.

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10,952 просмотров • 5 месяцев назад

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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 просмотров • 5 месяцев назад

Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:
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Robots are getting smarter, but most still fail the same way. They don’t learn from their own mistakes. A new paper proposes something different: a way for robots to self-improve directly from their failures in the real world. It’s called PLD (Probe, Learn, Distill). The idea: instead of collecting endless human demos, let the robot figure out where it fails, learn how to recover, and then distill that knowledge back into its main model. Key takeaways from the research: ✅ Uses residual reinforcement learning to recover from policy failures ✅ Achieves 99% success on LIBERO and 100% on real Franka and YAM arms ✅ Runs hour-long manipulation tasks without human resets ✅ Builds a feedback loop between real-world data and model adaptation Unlike supervised fine-tuning, which relies on humans, PLD learns from the robot’s own experience. By training on its own failure distribution, the model becomes both more efficient and more aligned with the real world. It’s not just a technical shift, it’s a step toward robots that improve themselves through real-world practice. Thanks for sharing, Wenli Xiao !! Paper and demos: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,881 просмотров • 10 месяцев назад

⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.
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⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.

Future Memo

10,138 просмотров • 1 месяц назад

Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path, the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:
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Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path, the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,127 просмотров • 6 месяцев назад

Fei-Fei Li ( Fei-Fei Li ) beautifully explains Robotics. She defines robotics not by form, like humanoids or cars, but by function: they are any "embodied machines" that must perceive, understand, and act within a physical, 3D space. This core requirement is "spatial intelligence," the unifying principle of all robotics, allowing them to perform tasks and even collaborate with humans. Throughout all of human history, we have been confined to a single, shared reality: the "physical Earth 3D world." This singularity has been our only playground. However, new technologies that combine 3D generation and reconstruction are shattering this limitation. We can now create "infinite universes"—a multiverse of digital worlds for countless purposes, from training robots to enabling creativity, travel, and storytelling. This leap from one physical world to an infinite multiverse unlocks boundless possibilities for human imagination and interaction. Video from a16z
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Fei-Fei Li ( Fei-Fei Li ) beautifully explains Robotics. She defines robotics not by form, like humanoids or cars, but by function: they are any "embodied machines" that must perceive, understand, and act within a physical, 3D space. This core requirement is "spatial intelligence," the unifying principle of all robotics, allowing them to perform tasks and even collaborate with humans. Throughout all of human history, we have been confined to a single, shared reality: the "physical Earth 3D world." This singularity has been our only playground. However, new technologies that combine 3D generation and reconstruction are shattering this limitation. We can now create "infinite universes"—a multiverse of digital worlds for countless purposes, from training robots to enabling creativity, travel, and storytelling. This leap from one physical world to an infinite multiverse unlocks boundless possibilities for human imagination and interaction. Video from a16z

Rohan Paul

51,174 просмотров • 4 месяцев назад

Fei-Fei Li ( Fei-Fei Li ) beautifully explains Robotics. She defines robotics not by form, like humanoids or cars, but by function: they are any "embodied machines" that must perceive, understand, and act within a physical, 3D space. This core requirement is "spatial intelligence," the unifying principle of all robotics, allowing them to perform tasks and even collaborate with humans. Throughout all of human history, we have been confined to a single, shared reality: the "physical Earth 3D world." This singularity has been our only playground. However, new technologies that combine 3D generation and reconstruction are shattering this limitation. We can now create "infinite universes"—a multiverse of digital worlds for countless purposes, from training robots to enabling creativity, travel, and storytelling. This leap from one physical world to an infinite multiverse unlocks boundless possibilities for human imagination and interaction. Video from a16z
1:25

Fei-Fei Li ( Fei-Fei Li ) beautifully explains Robotics. She defines robotics not by form, like humanoids or cars, but by function: they are any "embodied machines" that must perceive, understand, and act within a physical, 3D space. This core requirement is "spatial intelligence," the unifying principle of all robotics, allowing them to perform tasks and even collaborate with humans. Throughout all of human history, we have been confined to a single, shared reality: the "physical Earth 3D world." This singularity has been our only playground. However, new technologies that combine 3D generation and reconstruction are shattering this limitation. We can now create "infinite universes"—a multiverse of digital worlds for countless purposes, from training robots to enabling creativity, travel, and storytelling. This leap from one physical world to an infinite multiverse unlocks boundless possibilities for human imagination and interaction. Video from a16z

Rohan Paul

41,441 просмотров • 8 месяцев назад