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The table-to-dishwasher task is the classic nightmare scenario for roboticists: Long-horizon, highly dexterous, precise, whole-body manipulation combined with delicate, transparent, reflective, and deformable objects. Yet Memo handles it so naturally and elegantly.

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Tony Zhao

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802,304 просмотров • 8 месяцев назад •via X (Twitter)

ЮморОбразованиеНаука и технологии
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🚀🚀🚀 Ever wondered what it takes for robots to handle real-world household tasks? long-horizon execution, deformable object dexterity, and unseen object generalization — meet GR-3, ByteDance Seed’s new Vision-Language-Action (VLA) model! GR-3 is a generalizable Vision-Language-Action (VLA) model with strong capabilities in complex long-horizon tasks. It understands unseen abstract concepts, manipulates deformable objects robustly, and adapts to novel settings with minimal human data. ✨ Generalization: Generalizes well to unseen objects, environments, and even instructions with abstract concepts. ✨ Long-Horizon Manipulation: Completes long-horizon tasks with strong instruction-following capabilities. ✨ Deformable Object Manipulation: Manipulate deformable objects robustly. Project Page: Arxiv: #ByteDance #ByteDanceSeed #GR3 #VLA #Robotics #FoundationModels
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🚀🚀🚀 Ever wondered what it takes for robots to handle real-world household tasks? long-horizon execution, deformable object dexterity, and unseen object generalization — meet GR-3, ByteDance Seed’s new Vision-Language-Action (VLA) model! GR-3 is a generalizable Vision-Language-Action (VLA) model with strong capabilities in complex long-horizon tasks. It understands unseen abstract concepts, manipulates deformable objects robustly, and adapts to novel settings with minimal human data. ✨ Generalization: Generalizes well to unseen objects, environments, and even instructions with abstract concepts. ✨ Long-Horizon Manipulation: Completes long-horizon tasks with strong instruction-following capabilities. ✨ Deformable Object Manipulation: Manipulate deformable objects robustly. Project Page: Arxiv: #ByteDance #ByteDanceSeed #GR3 #VLA #Robotics #FoundationModels

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46,323 просмотров • 1 год назад

I've been working on deformable object manipulation since my PhD. It was totally a nightmare years ago and my PhD advisor was telling me not to work on it for my own good. Today, at ByteDance Seed, we are dropping GR-RL, a new VLA+RL system that manages long-horizon precise dexterous manipulation of deformable objects. This is probably the first real-world RL system to make a robot: ✅ Lace up your shoes end to end ✅ Hit millimeter tolerance repeatedly ✅ Recover from mistakes (See video!) ✅ And complete continuous shoelace threading on a real bimanual platform 📈 Success rate: ↑ from 45.7% → 83.3% Yes, robots can now actually do this. Project page: ArXiv:
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I've been working on deformable object manipulation since my PhD. It was totally a nightmare years ago and my PhD advisor was telling me not to work on it for my own good. Today, at ByteDance Seed, we are dropping GR-RL, a new VLA+RL system that manages long-horizon precise dexterous manipulation of deformable objects. This is probably the first real-world RL system to make a robot: ✅ Lace up your shoes end to end ✅ Hit millimeter tolerance repeatedly ✅ Recover from mistakes (See video!) ✅ And complete continuous shoelace threading on a real bimanual platform 📈 Success rate: ↑ from 45.7% → 83.3% Yes, robots can now actually do this. Project page: ArXiv:

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110,385 просмотров • 8 месяцев назад

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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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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Scaling vision-language-action (VLA) models to high-DoF dexterous hands has long been a "holy grail" challenge due to the high-dimensional action space and data scarcity. As a wrap up of the year 2025, we are releasing GR-Dexter, a holistic hardware-model-data framework for generalist manipulation on a bimanual dexterous-hand robot. This is the first VLA system to achieve: ✅ High-DoF Control: Managing a 56-DoF bimanual system (21-DoF per hand). ✅ Long-Horizon Tasks with tool use: Vacuuming, bread serving with tongs, and table decluttering. ✅ Open-World Generalization: Robust performance with unseen objects and abstract instructions. Project page: ArXiv:
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Scaling vision-language-action (VLA) models to high-DoF dexterous hands has long been a "holy grail" challenge due to the high-dimensional action space and data scarcity. As a wrap up of the year 2025, we are releasing GR-Dexter, a holistic hardware-model-data framework for generalist manipulation on a bimanual dexterous-hand robot. This is the first VLA system to achieve: ✅ High-DoF Control: Managing a 56-DoF bimanual system (21-DoF per hand). ✅ Long-Horizon Tasks with tool use: Vacuuming, bread serving with tongs, and table decluttering. ✅ Open-World Generalization: Robust performance with unseen objects and abstract instructions. Project page: ArXiv:

Xiao Ma

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Agility Robotics has developed a whole-body control foundation model for their humanoid robot, Digit, acting like a "motor cortex" to ensure safe, stable performance in diverse tasks. This small neural network (<1M parameters) is trained in NVIDIA's Isaac Sim for simulated decades over 3-4 days, transferring zero-shot to the real world. It handles balance, smooth motion, and precision manipulation while preventing falls. Prompted with free-space positions/orientations for arms and torso, it enables walking, picking/placing heavy objects, and robustness to disturbances. Downstream, it supports dexterous skills and LLM-coordinated behaviors.
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Agility Robotics has developed a whole-body control foundation model for their humanoid robot, Digit, acting like a "motor cortex" to ensure safe, stable performance in diverse tasks. This small neural network (<1M parameters) is trained in NVIDIA's Isaac Sim for simulated decades over 3-4 days, transferring zero-shot to the real world. It handles balance, smooth motion, and precision manipulation while preventing falls. Prompted with free-space positions/orientations for arms and torso, it enables walking, picking/placing heavy objects, and robustness to disturbances. Downstream, it supports dexterous skills and LLM-coordinated behaviors.

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Chinese robotics company Galaxea Dynamics has introduced a robotic hand called Dexo. It is a four-finger dexterous hand with 17 degrees of freedom for smooth and precise movement. It can sense light touch and handle up to 1 kg with one fingertip, making it useful for both delicate and demanding tasks.

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How to chain multiple dexterous skills to tackle complex long-horizon manipulation tasks? Imagine retrieving a LEGO block from a pile, rotating it in-hand, and inserting it at the desired location to build a structure. Introducing our new work - Sequential Dexterity 🧵👇
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How to chain multiple dexterous skills to tackle complex long-horizon manipulation tasks? Imagine retrieving a LEGO block from a pile, rotating it in-hand, and inserting it at the desired location to build a structure. Introducing our new work - Sequential Dexterity 🧵👇

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A few weeks ago, we shared our progress on articulated objects and long-horizon tasks. Here are two representative examples: - We've been steadily expanding our asset library to cover more articulated objects. Articulated objects have always been a challenging asset class to handle in simulation. Interacting with them requires robots to master atomic skills such as pushing, pulling, opening, and closing, and to understand part structure, interaction constraints, and how the object moves. - Long-horizon tasks can now be generated at scale. Long-horizon tasks are the other hard category: they require chaining multiple sub-goals in sequence. A failure early in the task can cascade and make the rest unrecoverable. Axis is scaling along three dimensions at once: data volume, data quality, and task difficulty.

Axis Robotics

13,700 просмотров • 1 месяц назад

Shenzhen-based Kinetix AI has introduced a faceless humanoid robot called KAI. The robot is about 5 feet 8 inches tall, weighs 70 kilograms, and can carry loads up to 20 kilograms while running for around 4 hours on a single charge. With highly flexible hands and precise movements, KAI can fold clothes, handle delicate objects, and assist with everyday household tasks.
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Shenzhen-based Kinetix AI has introduced a faceless humanoid robot called KAI. The robot is about 5 feet 8 inches tall, weighs 70 kilograms, and can carry loads up to 20 kilograms while running for around 4 hours on a single charge. With highly flexible hands and precise movements, KAI can fold clothes, handle delicate objects, and assist with everyday household tasks.

Space and Technology

22,865 просмотров • 2 месяцев назад

Robostral can now follow natural language instructions. It responds to voice commands and pointing. It is also getting better at fine-grained manipulation where precise force control matters. It generalizes to new objects and tasks not present in the training data.
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Robostral can now follow natural language instructions. It responds to voice commands and pointing. It is also getting better at fine-grained manipulation where precise force control matters. It generalizes to new objects and tasks not present in the training data.

Olivier Duchenne

51,437 просмотров • 2 месяцев назад

Humanoid robots playing table tennis fully autonomously. The 'HITTER' system combines a model-based planner with a reinforcement learning (RL) whole-body controller. It is fully autonomous but relies on an external sensing system. A 9-camera OptiTrack motion capture setup tracks the ball, which is covered with reflective markers, to predict its precise trajectory and compute the optimal strike position, velocity, and timing. The RL controller then executes coordinated arm, leg, and waist movements, producing agile, human-like returns.
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Humanoid robots playing table tennis fully autonomously. The 'HITTER' system combines a model-based planner with a reinforcement learning (RL) whole-body controller. It is fully autonomous but relies on an external sensing system. A 9-camera OptiTrack motion capture setup tracks the ball, which is covered with reflective markers, to predict its precise trajectory and compute the optimal strike position, velocity, and timing. The RL controller then executes coordinated arm, leg, and waist movements, producing agile, human-like returns.

The Humanoid Hub

91,896 просмотров • 11 месяцев назад

In just ~3 months, as a solo founder with no prior robotics experience, General Trajectory trained a foundation model for dexterous manipulation that lets humanoid robots pick up unseen objects and perform real-world work. It generalizes to novel objects and scenes, including cases where prior SoTA models achieve 0% success. Congrats on the launch Joshua!
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In just ~3 months, as a solo founder with no prior robotics experience, General Trajectory trained a foundation model for dexterous manipulation that lets humanoid robots pick up unseen objects and perform real-world work. It generalizes to novel objects and scenes, including cases where prior SoTA models achieve 0% success. Congrats on the launch Joshua!

Y Combinator

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Excited to introduce StereoPolicy, led by Evans Han. 📷📷🤖StereoPolicy is an effective way to add geometric cues to modern robot policy models while keeping the strengths of pretrained 2D encoders. ⁉️Why stereo for robot manipulation? Monocular RGB often lacks the depth cues needed for precise manipulation, while RGB-D and point clouds can be noisy or brittle, especially on reflective and transparent objects in real-world deployment. Instead of explicitly reconstructing disparity, depth, or point clouds, StereoPolicy directly fuses synchronized left/right RGB views to learn implicit stereo cues, avoiding extra reconstruction latency that can make real-time manipulation difficult. Project Page:

Ruohan Zhang

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Q: To be clear, is sanctions relief for Iran on the table? BESSENT: Nothing is going to be on the table until we see the Strait of Hormuz open and the Iranians agree that they have to turn over the highly enriched uranium Q: So it is on the table
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Q: To be clear, is sanctions relief for Iran on the table? BESSENT: Nothing is going to be on the table until we see the Strait of Hormuz open and the Iranians agree that they have to turn over the highly enriched uranium Q: So it is on the table

Aaron Rupar

37,637 просмотров • 2 месяцев назад

📢 Announcing one of the most exciting works from us this year on **scalable robot policy evaluation through real-to-sim transfer**, moving toward a scalable evaluation engine with structured world models that capture the appearance, geometry, and dynamics of environments involving deformable objects. 🤖 Evaluation remains one of the biggest bottlenecks in building general-purpose robots. Today, robots are still evaluated only in the real world, which is **orders of magnitude slower** than the development of language agents. We propose a new framework where simulation performance **strongly correlates** with the real world (r > 0.9), even for deformable objects. The key difference from existing work lies in the correlation between simulation and reality: if a robot model performs better in the digital world, does it also perform better in the real world? This question has long made people hesitant about simulation-based evaluation — especially for deformable objects. We are changing that. Our pipeline achieves effective real-to-sim transfer, establishing **state-of-the-art correlation** between simulation and reality for deformable object manipulation. It provides a **scalable and reproducible evaluation engine** for robot learning. 🌐
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📢 Announcing one of the most exciting works from us this year on **scalable robot policy evaluation through real-to-sim transfer**, moving toward a scalable evaluation engine with structured world models that capture the appearance, geometry, and dynamics of environments involving deformable objects. 🤖 Evaluation remains one of the biggest bottlenecks in building general-purpose robots. Today, robots are still evaluated only in the real world, which is **orders of magnitude slower** than the development of language agents. We propose a new framework where simulation performance **strongly correlates** with the real world (r > 0.9), even for deformable objects. The key difference from existing work lies in the correlation between simulation and reality: if a robot model performs better in the digital world, does it also perform better in the real world? This question has long made people hesitant about simulation-based evaluation — especially for deformable objects. We are changing that. Our pipeline achieves effective real-to-sim transfer, establishing **state-of-the-art correlation** between simulation and reality for deformable object manipulation. It provides a **scalable and reproducible evaluation engine** for robot learning. 🌐

Yunzhu Li

39,960 просмотров • 9 месяцев назад

Humanoid Long Jump. The G1 humanoid from Unitree Robotics has completed a long jump of 1.4 meters - possibly the longest jump ever achieved by a humanoid robot of its size in the world, standing only 1.32 meters tall. The G1 has up to 43 joint motors and a dexterous three-fingered hand, designed for precise manipulation and AI research. Priced from $16,000, it features robust Sim2Real capabilities, making it a compelling choice for robotics developers.
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Humanoid Long Jump. The G1 humanoid from Unitree Robotics has completed a long jump of 1.4 meters - possibly the longest jump ever achieved by a humanoid robot of its size in the world, standing only 1.32 meters tall. The G1 has up to 43 joint motors and a dexterous three-fingered hand, designed for precise manipulation and AI research. Priced from $16,000, it features robust Sim2Real capabilities, making it a compelling choice for robotics developers.

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🚀 Unitree open-sources UnifoLM-WBT-Dataset — a high-quality real-world humanoid robot whole-body teleoperation (WBT) dataset for open environments. 🥳Publicly available since March 5, 2026, the dataset will continue to receive high-frequency rolling updates. It aims to establish the most comprehensive real-world humanoid robot dataset in terms of scenario coverage, task complexity, and manipulation diversity. 👉 Explore the dataset here:

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5,632,399 просмотров • 4 месяцев назад

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Galbot

45,830 просмотров • 5 месяцев назад