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Figure’s Helix 2.5 shows how pretraining on human behavior data can improve a humanoid robot’s ability to work in unfamiliar environments. In Figure’s test, the Index-pretrained model achieved 56% zero-shot success across 30 unseen homes, compared with just 9% for a model trained from scratch. The key takeaway is...

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Profilbild von モモフ好き 日本には酸っぱい🍋がいっぱいw
モモフ好き 日本には酸っぱい🍋がいっぱいwvor 10 Tagen

Helixは、米Figure AIが開発したヒューマノイドロボット向けの最先端のVision-Language-Action（VLA）AI基盤

Profilbild von Patriota Volta
Patriota Voltavor 11 Tagen

56% vs 9% just from pretraining on human behavior. the gap is ridiculous

Profilbild von lulu
luluvor 10 Tagen

Treat the robot with respect. Also it's very slow.

Profilbild von Nick Champrenault
Nick Champrenaultvor 11 Tagen

56% against 9% is a real gap and still a coin flip in someone's kitchen. the number that decides deployment is which 44% failed and whether those failures cluster. random failure and dark-countertop failure are different problems.

Profilbild von Crogon
Crogonvor 11 Tagen

i cannot be the only one who finds that ai voice the most annoying among them all.

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First there was Skild AI showing zero-shot transfer to unfamiliar homes, now Figure has a humanoid doing whole-body tasks across 30 homes it’s never seen before. Helix 2.5 was pretrained from scratch on Index, Figure’s human-behavior dataset, then fine-tuned for those three tasks elsewhere. Zero-shot meaning new homes and objects, not tasks it was never trained to do. No data collection or fine-tuning in those 30 homes. adding Index pretraining raised complete task success from 9% to 56%, with no partial credit. They also found that doubling the human pretraining data predictably improved robot-action prediction, enough to forecast their largest run’s loss to four decimal places. Obviously 56% isn’t perfect, but getting that much more generalization from human experience alone is pretty exciting.
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First there was Skild AI showing zero-shot transfer to unfamiliar homes, now Figure has a humanoid doing whole-body tasks across 30 homes it’s never seen before. Helix 2.5 was pretrained from scratch on Index, Figure’s human-behavior dataset, then fine-tuned for those three tasks elsewhere. Zero-shot meaning new homes and objects, not tasks it was never trained to do. No data collection or fine-tuning in those 30 homes. adding Index pretraining raised complete task success from 9% to 56%, with no partial credit. They also found that doubling the human pretraining data predictably improved robot-action prediction, enough to forecast their largest run’s loss to four decimal places. Obviously 56% isn’t perfect, but getting that much more generalization from human experience alone is pretty exciting.

Chris

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What happens when robot world models learn from human experience at scale? 🤔 DreamDojo from NVIDIA Research is a generalist robot world model pretrained on 44K hours of egocentric human videos and then post-trained on robot data to generalize across new objects and environments. After distillation, it runs at 10 FPS for live teleoperation, policy evaluation, and model-based planning. Read the ICML paper to learn more 📄
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AI training faces a very different set of trade-offs compared to human evolution. Because you can directly copy a trained model basically for free, it makes sense to amortize in the pre-trained weights the learning that humans spread across a lifetime.
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AI training faces a very different set of trade-offs compared to human evolution. Because you can directly copy a trained model basically for free, it makes sense to amortize in the pre-trained weights the learning that humans spread across a lifetime.

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25,747 Aufrufe • vor 5 Monaten

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Egocentric Videos for Robot Training 🧵 Researchers at NYU and UC Berkeley published research where they developed a system called EgoZero that trains robots using human demonstration videos recorded with smart glasses. 👓 The system converts first-person human actions into 3D point-based state-action representations. The policies executed on a gripper-equipped robot, achieved a 70% zero-shot success rate across seven manipulation tasks, with only 20 minutes of human data per task. EgoZero stands as one of the solid empirical proofs that egocentric smart-glass video collected from everyday human behavior can serve as powerful, scalable training data for real robot learning. Vincent Liu Ademi Adeniji

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The problem with humanoid teleoperation is that it is expensive and difficult to scale Enter NVIDIA's EgoScale: - A VLA model pretrained on thousands hours of egocentric human videos. - Mid-trained via 50 hours of human + 4 hours of robot "play" data for human-robot alignment. - Fine-tuned with very few examples of task-specific robot teleoperation (100 or fewer per task). - Successfully transfers across 5-finger (Sharpa) and 3-finger (Unitree G1) robot hands. - Performance scales predictably as data increases.
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NVIDIA just announced EgoScale 🤖🧠 NVIDIA Research has uncovered a log-linear scaling law for robot dexterity by pretraining VLA models on over 20,000 hours of egocentric human video This massive dataset is 20 times larger than previous efforts and proves that robot intelligence follows a predictable path: the more human data, the lower the loss The secret is a simple recipe combining large-scale human pretraining with a small amount of aligned human-robot mid-training to bridge the gap In testing, this method boosted the average success rate by 54% on a 22-DoF robotic hand compared to policies built without pretraining EgoScale also enables one-shot task adaptation and works across different hardware, suggesting that human motion is a universal motor prior for robots Website: Paper: Source: NVIDIA Research #Robot #Humanoid #Robotics #AI #EmbodiedAI #PhysicalAI #NVIDIA #EgoScale #GR00T

RoboHub🤖

43,752 Aufrufe • vor 7 Monaten

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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Quadrupeds are fast. Agile. Great at locomotion. But can they manipulate? A new approach from Carnegie Mellon University, Google DeepMind, and Bosch is teaching quadrupedal robots to do more than walk, they’re learning to interact. It’s called Human2LocoMan: a system that uses human data to pretrain robot policies before finetuning on real hardware. The result? A four-legged robot that can walk, carry, organize, scoop, and sort with both single and dual-arm control. By pretraining on human motion, they cut the amount of robot data in half—while improving success rates by over 80% in unfamiliar environments. Their Modularized Cross-Embodiment Transformer (MXT) learns from both human and robot demonstrations, then generalizes those skills to physical tasks—no hardcoded behaviors required. It’s locomotion and manipulation. A quadruped that can walk and clean up after itself?

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Brain-controlled exoskeletons to train humanoid robots! 🧠 Fourier just presented human tele-operators using brain control interfaces and exoskeletal arms to train humanoid robots on home tasks. The brain control interface is the interesting part. Instead of using a controller or joystick to teleoperate, the operator's movements and intentions are captured more naturally through the exoskeleton and BCI. This means the demonstrations are more fluid, more human-like, and better suited for training robots to perform delicate home tasks. Multiple tele-operators are simultaneously generating training data across multiple robots. This is how you build the dataset needed for eventual full autonomy, without waiting years for it to arrive. This might be the bridge between "robots that work in controlled environments" and "robots that work in homes." Not full autonomy right away, but trusted human intelligence operating through a robot body, getting better with every task completed. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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21,042 Aufrufe • vor 8 Monaten

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In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)

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12,045 Aufrufe • vor 1 Jahr

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Train robots. Earn rewards. Base is pioneering the Train-to-Earn model for robotics. With Axis Robotics, anyone can help train robot simulations from a laptop and be rewarded for contributing data. The future of robotics won’t be trained by a handful of companies. It’ll be trained by everyone. PL 漂亮 (✱,✱) showcased this vision at SuperAI, the largest AI event in Asia 👇

Base APAC

18,980 Aufrufe • vor 3 Monaten