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Legged Locomotion… meets Skateboarding [Paper ⬇️] Most robot movement models either rely on fixed patterns or struggle to handle complex changes. DHAL (Discrete-time Hybrid Automata Learning) takes a different approach: using reinforcement learning to teach robots when and how to switch movements in real-time: ✅ Learns when to switch... between different motions without pre-labeled data ✅ Handles complex, high-dimensional movements like a quadrupedal robot on a skateboard ✅ Uses a multi-critic architecture to improve contact-based motion control ✅ Works in both simulation and real-world environments with strong results It proves that robots can learn movement transitions on their own, without predefined rules. Paper: Thanks to Hang Liu for bringing this to my attention!show more

Ilir Aliu - eu/acc

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41,894 görüntüleme • 1 yıl önce •via X (Twitter)

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am i suzu or am i selene? profil fotoğrafı

am i suzu or am i selene?1 yıl önce

teach him how to ollie!!

Yang profil fotoğrafı

Yang1 yıl önce

Want to learn how practical AI skills and automations for your business and work? Check out our 50+ step-by-step video tutorials 100% FREE 20+ hours of Ai and Automation goodness absolutely free 🥳

𐌍𝕺𐌀𐋅- e/acc profil fotoğrafı

𐌍𝕺𐌀𐋅- e/acc1 yıl önce

I was waiting for a kickflip

Dylan profil fotoğrafı

Dylan1 yıl önce

what other kinds of movement transitions would this apply to (or is it pretty generalized)?

1̦̂0̿͜0̜̚M̱̓o̺͌n͖̒k̯̒e̞͠ỷ͈s̖̅ 🧌🙉 profil fotoğrafı

1̦̂0̿͜0̜̚M̱̓o̺͌n͖̒k̯̒e̞͠ỷ͈s̖̅ 🧌🙉1 yıl önce

what is battery-life? You guys never tell us. It must be a weakspot then🤪

Dmitry M profil fotoğrafı

Dmitry M1 yıl önce

@Jac5Connor ... in case you need a skateboarding companion it is only $3K (my better half banned robots from our house :-) )

Michael Scarano profil fotoğrafı

Michael Scarano1 yıl önce

put him on a snowboard so he can go down the slopes w/ me

Lol Nope profil fotoğrafı

Lol Nope1 yıl önce

They’re just pissing all over @BostonDynamics at this point. 😂

"Normally Rather Pleasant" profil fotoğrafı

"Normally Rather Pleasant"1 yıl önce

lol lmao, even. This still needs 2 more legs, full above-body range-of-motion, and better feet before I do anything but laugh at it. Robodogs have no real-world application other than as plastic consumer waste, in this state.

; profil fotoğrafı

;1 yıl önce

they are having too much fun, make one that does some chores

🗣 📣 UBI NOW profil fotoğrafı

🗣 📣 UBI NOW1 yıl önce

📝

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Excited to share our latest work: “Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots” 🪲🦎 We show that Hebbian learning outperforms LSTM-based adaptation for real-world transfer. It even works without domain randomization! It can handle: ✅ Uneven terrain ✅ Morphological damage ✅ Sim-to-real gaps

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A new paper by Disney Research introduces a 2-stage technique for motion tracking in physics-based character animation, using a pretrained latent space and RL to control diverse and unseen motions on both virtual and real-world humanoid robots. Paper:

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The Humanoid Hub

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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

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How precise can a robot get… without ever seeing the object move? Turns out, just a geometric model is enough. A robot hit 73 precise 3D goals in a row, using only a geometric model. No demonstrations. No imitation learning. No fancy vision models. The secret? Sample where the robot could make contact. Let local contact-implicit control take over from there. That’s it. ✅ Real-time performance ✅ Natural contact making and breaking ✅ Works on tricky tasks like pushing and rotating This approach mixes global planning with local MPC. Fast. Accurate. Sample-efficient. Thanks for sharing, Michael Posa ! 📍Watch it in action: Paper:

How precise can a robot get… without ever seeing the object move? Turns out, just a geometric model is enough. A robot hit 73 precise 3D goals in a row, using only a geometric model. No demonstrations. No imitation learning. No fancy vision models. The secret? Sample where the robot could make contact. Let local contact-implicit control take over from there. That’s it. ✅ Real-time performance ✅ Natural contact making and breaking ✅ Works on tricky tasks like pushing and rotating This approach mixes global planning with local MPC. Fast. Accurate. Sample-efficient. Thanks for sharing, Michael Posa ! 📍Watch it in action: Paper:

Ilir Aliu

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

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

Space and Technology

22,052 görüntüleme • 13 gün önce

In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

RoboPapers

18,813 görüntüleme • 5 ay önce

Robot dog learned dexterity! 🤯 Robots usually struggle to move and handle objects at the same time, a challenge called loco-manipulation. Researchers at the RAI Institute have developed a new system called ReLIC that makes this easier. Instead of giving robots fixed roles for arms and legs, ReLIC lets them switch roles on the fly, for example, using a leg to help lift something while balancing on the others. This approach is more natural, similar to how humans use different body parts together without thinking about it. ReLIC was tested on a four-legged robot, which successfully performed tasks like moving boxes, opening drawers, and placing items into containers.

Robot dog learned dexterity! 🤯 Robots usually struggle to move and handle objects at the same time, a challenge called loco-manipulation. Researchers at the RAI Institute have developed a new system called ReLIC that makes this easier. Instead of giving robots fixed roles for arms and legs, ReLIC lets them switch roles on the fly, for example, using a leg to help lift something while balancing on the others. This approach is more natural, similar to how humans use different body parts together without thinking about it. ReLIC was tested on a four-legged robot, which successfully performed tasks like moving boxes, opening drawers, and placing items into containers.

Lukas Ziegler

19,743 görüntüleme • 9 ay önce