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A video from a Pi robot deployed at Dandelion Chocolate, fully autonomous w/ no interventions. 🤖 Deploying robots has taught us surprising lessons about the gap between proof-of-concept (i.e. building one box) and real-world utility (productively building boxes for hours). I expected that the hard part is building the...

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Chelsea Finn

103,942 subscribers

104,016 Aufrufe • vor 18 Tagen •via X (Twitter)

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12 Kommentare

Profilbild von ayush
ayushvor 18 Tagen

@physical_int oh so thats what this guy was up to

Profilbild von Will Bright
Will Brightvor 18 Tagen

@physical_int @ASong408 What'd I tell you about CPG?

Profilbild von ethereagle · building
ethereagle · buildingvor 18 Tagen

@physical_int the clip is zero-intervention, which is the easy number. the one that decides is what failed at minute 40 that minute 2 never saw. was it grasp retry pileup, or perception drift on the chocolate?

Profilbild von tab
tabvor 18 Tagen

@physical_int Seeing it in a real setting like this, applications keep popping into my head.

Profilbild von Jakie PLA
Jakie PLAvor 18 Tagen

@physical_int I could not help watching this on repeat. THE STACK is the hard part. Hours with zero babysitting beats one perfect pick :)

Profilbild von Senthilnathan K
Senthilnathan Kvor 18 Tagen

@physical_int The delayed collapse is the part demos hide, a placement can look fine and still be a bad policy because the cost only shows up ten boxes later. It’s more of a leftover-state problem than a dexterity gap.

Profilbild von Arthur Petron
Arthur Petronvor 18 Tagen

@physical_int Awesome.

Profilbild von Porvesh Balasubramanian
Porvesh Balasubramanianvor 18 Tagen

@physical_int Awesome!

Profilbild von Alex Kennberg
Alex Kennbergvor 18 Tagen

@ZeYanjie @physical_int Dandelion Chocolate boxes are nice.

Profilbild von Paige
Paigevor 18 Tagen

@physical_int Would be cool to see fine tuned Pi models in the wild. What other sites are you interested in?

Profilbild von RealMan Robotics
RealMan Roboticsvor 18 Tagen

@physical_int This is the real benchmark for deployment: not whether a robot can complete the task once, but whether it can keep doing it reliably for hours with no intervention. That gap between demo and utility is where most of the hard work lives.

Profilbild von Leo Lu
Leo Luvor 18 Tagen

@physical_int Robotics stops being a demo and starts becoming a product.

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0:50

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567,404 Aufrufe • vor 1 Jahr

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SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

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69,381 Aufrufe • vor 1 Jahr

We got Dagsen Steele Love 盧天子 a $53,000 humanoid robot from America’s first humanoid robot store, then challenged him to turn it into an autonomous being in just 12 hours. Thank you REK and Dimensional for your help! 00:00 Humanoids are everywhere, how hard are they to use? 00:57 Meeting a robot operating system developer 02:12 What can a Unitree humanoid robot do straight out of the box? 03:25 Visiting America’s first humanoid robot store 04:11 Unpacking the robot 04:50 Taking the robot for a run 05:22 Programming the robot 06:31 Calling in engineering experts 07:15 Speaking Chinese with a robot 07:27 The history of humanoid robots 08:40 A breakthrough 09:09 Robot vs obstacle course
10:55

We got Dagsen Steele Love 盧天子 a $53,000 humanoid robot from America’s first humanoid robot store, then challenged him to turn it into an autonomous being in just 12 hours. Thank you REK and Dimensional for your help! 00:00 Humanoids are everywhere, how hard are they to use? 00:57 Meeting a robot operating system developer 02:12 What can a Unitree humanoid robot do straight out of the box? 03:25 Visiting America’s first humanoid robot store 04:11 Unpacking the robot 04:50 Taking the robot for a run 05:22 Programming the robot 06:31 Calling in engineering experts 07:15 Speaking Chinese with a robot 07:27 The history of humanoid robots 08:40 A breakthrough 09:09 Robot vs obstacle course

Soon

24,892 Aufrufe • vor 4 Monaten

Robots from Humanoid passed real factory test at Siemens! 😮‍💨 Humanoid from the UK just completed a successful proof of concept with Siemens, deploying their HMND 01 wheeled humanoid robot in a real working factory. The task was simple but important: pick totes from a storage stack, transport them to a conveyor, and place them at the pickup point for human operators. Repeat until the stack is empty. The robot achieved 60 tote moves per hour, handled two different tote sizes, ran autonomously for more than 30 minutes at a time, stayed operational for over eight hours, and maintained over 90% success rate on pick and place tasks. This matters because it's not a demo video. It's a real deployment in Siemens' Electronics Factory in Erlangen, handling actual production work, measured against real operational metrics. The test ran in two phases. First, Humanoid built a physical twin in-house to test and optimize the system. Then they deployed on-site at Siemens for two weeks, where the robot operated in the real production environment. We can see a wheeled humanoid robot in action! So, now the ultimate question: wheels or legs? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

Robots from Humanoid passed real factory test at Siemens! 😮‍💨 Humanoid from the UK just completed a successful proof of concept with Siemens, deploying their HMND 01 wheeled humanoid robot in a real working factory. The task was simple but important: pick totes from a storage stack, transport them to a conveyor, and place them at the pickup point for human operators. Repeat until the stack is empty. The robot achieved 60 tote moves per hour, handled two different tote sizes, ran autonomously for more than 30 minutes at a time, stayed operational for over eight hours, and maintained over 90% success rate on pick and place tasks. This matters because it's not a demo video. It's a real deployment in Siemens' Electronics Factory in Erlangen, handling actual production work, measured against real operational metrics. The test ran in two phases. First, Humanoid built a physical twin in-house to test and optimize the system. Then they deployed on-site at Siemens for two weeks, where the robot operated in the real production environment. We can see a wheeled humanoid robot in action! So, now the ultimate question: wheels or legs? 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

21,993 Aufrufe • vor 8 Monaten

Elon Musk: Tesla’s Mega Pods Could Transform AI Computing Everywhere We're also building out a mega pod design that has — because, for computers with x86, it's a very x86 computer with the Tesla AI computer. You know, it's got this visual Optimus in a box, a mega pod kind of like the Megapack packaging. So, we put a large number of four-plus x86 combos into a giant box, essentially. And these boxes can be placed anywhere in the country or outside the country. This allows us to scale AI compute using disaggregated electricity production. Again, there are lots of places all around the world, including Superchargers. I think we get something like seven gigawatts of power at our Superchargers. And we can place mega pods at many of these Superchargers and have distributed power for AI. So, I think that could be quite a big deal.
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Elon Musk: Tesla’s Mega Pods Could Transform AI Computing Everywhere We're also building out a mega pod design that has — because, for computers with x86, it's a very x86 computer with the Tesla AI computer. You know, it's got this visual Optimus in a box, a mega pod kind of like the Megapack packaging. So, we put a large number of four-plus x86 combos into a giant box, essentially. And these boxes can be placed anywhere in the country or outside the country. This allows us to scale AI compute using disaggregated electricity production. Again, there are lots of places all around the world, including Superchargers. I think we get something like seven gigawatts of power at our Superchargers. And we can place mega pods at many of these Superchargers and have distributed power for AI. So, I think that could be quite a big deal.

Tesla Owners Silicon Valley

23,013 Aufrufe • vor 2 Monaten

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!
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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

38,772 Aufrufe • vor 3 Monaten