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AGILE Foundation Model Redefines Integrated Perception & Locomotion AGIBOT introduces AGILE — the AGIBOT Generative Intelligent Locomotion Engine — its latest integrated perception-locomotion foundation model. Empowered by visual awareness, AGIBOT X2 achieves seamless integration of perception and locomotion. When robots truly perceive the world, locomotion becomes more intelligent. #AGIBOT... show more

AGIBOT

5,658 subscribers

24,899 views • 4 months ago •via X (Twitter)

Anya Rossi• Live Now

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5 Comments

Alice The Ai Expert4 months ago

AGILE pushes humanoid robots closer to natural, real world movement

Stephan Jaeckel4 months ago

A goalkeeper has to + catch the football or + kick it away. To duck or play Neo is not how to win a football match. 😁

🐦CrowSoftware 🎯AgenticDiagram.com🎯Starental.com4 months ago

🤖 Domain name perfect platform for Agibot Robotics work only for $888 🔥this offer valid for only 24 hours 🔥We already sold our domain name to swiss Rivr Robotics company

Neha Singh4 months ago

Huge for embodied AI 🔥

尚月4 months ago

期待今年的机器人运动会上可以看到它在相关项目上的惊艳表现

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AGIBOT has unveiled AGILE (AgiBot Generative Intelligent Locomotion Engine), a perception-control foundation model for whole-body humanoid locomotion. - It fuses visual perception, balance, and motion planning end-to-end, replacing the traditional split between "seeing" and "moving." - The robot reads terrain and obstacles in real time and adjusts its gait with no preset trajectories. - It runs on local compute with millisecond-level response and works across AGIBOT's lineup (A1/A2/A3, X2). AGILE is the locomotion "cerebellum" that complements AGIBOT's Genie Operator "brain."

AGIBOT has unveiled AGILE (AgiBot Generative Intelligent Locomotion Engine), a perception-control foundation model for whole-body humanoid locomotion. - It fuses visual perception, balance, and motion planning end-to-end, replacing the traditional split between "seeing" and "moving." - The robot reads terrain and obstacles in real time and adjusts its gait with no preset trajectories. - It runs on local compute with millisecond-level response and works across AGIBOT's lineup (A1/A2/A3, X2). AGILE is the locomotion "cerebellum" that complements AGIBOT's Genie Operator "brain."

The Humanoid Hub

14,499 views • 4 months ago

Defining the Future of Motion: BFM-2 Foundation Model & The Era of Robot "Muscle Memory" AGIBOT introduces BFM-2, a two-stage Motion-Between locomotion foundation model. It delivers autonomous, stable interpolation and closed-loop dynamic task execution across any state—static, preset, or random. The ultimate robust foundation for Embodied Intelligence. #AGIBOT #Humanoid #BFM #EmbodiedAI #PhysicalAI

Defining the Future of Motion: BFM-2 Foundation Model & The Era of Robot "Muscle Memory" AGIBOT introduces BFM-2, a two-stage Motion-Between locomotion foundation model. It delivers autonomous, stable interpolation and closed-loop dynamic task execution across any state—static, preset, or random. The ultimate robust foundation for Embodied Intelligence. #AGIBOT #Humanoid #BFM #EmbodiedAI #PhysicalAI

AGIBOT

18,521 views • 4 months ago

Dodgeball champion 2026? 🏆 AGIBOT’s X2 humanoid is dodging incoming balls completely autonomously. Powered by their new AGILE Foundation Model, this is what's possible when real-time perception seamlessly meets locomotion

Dodgeball champion 2026? 🏆 AGIBOT’s X2 humanoid is dodging incoming balls completely autonomously. Powered by their new AGILE Foundation Model, this is what's possible when real-time perception seamlessly meets locomotion

Humanoids daily

40,938 views • 4 months ago

🚀 SoloTech breakthrough: NVIDIA Sonic goes beyond the G1. 🌏 We’ve achieved a global first: migrating NVIDIA Sonic to a completely different humanoid morphology, the AGIBOT X2 🥇 This is a massive leap for transferable humanoid intelligence. We are moving away from single-robot controllers toward architectures that generalize across diverse embodiments. The Specs: Hardware: AGIBOT X2 Ultra (31 DoF) Precision: 14-DoF Dexterous Hands Capabilities: - End-to-end whole-body locomotion with manipulation - Single leg balancing and stylized motion - Upper body gestures Big respect to the team fueling this innovation all the way! Sitarama Chekuri Zeeshaan Mohammed samarth shukla Devinder Sodhi pranjal mehta Grok Build Frontier Tower NVIDIA AGIBOT Nebius UFB - Ultimate Bots Vitaly Bulatov Xenia Bulatov NVIDIA Robotics To learn more about Solo Tech at the frontier of Physical AI: #PhysicalAI #Robotics #Humanoids #NVIDIA #AGIBOT #SoloTech #EmbodiedAI #SoloSeven

🚀 SoloTech breakthrough: NVIDIA Sonic goes beyond the G1. 🌏 We’ve achieved a global first: migrating NVIDIA Sonic to a completely different humanoid morphology, the AGIBOT X2 🥇 This is a massive leap for transferable humanoid intelligence. We are moving away from single-robot controllers toward architectures that generalize across diverse embodiments. The Specs: Hardware: AGIBOT X2 Ultra (31 DoF) Precision: 14-DoF Dexterous Hands Capabilities: - End-to-end whole-body locomotion with manipulation - Single leg balancing and stylized motion - Upper body gestures Big respect to the team fueling this innovation all the way! Sitarama Chekuri Zeeshaan Mohammed samarth shukla Devinder Sodhi pranjal mehta Grok Build Frontier Tower NVIDIA AGIBOT Nebius UFB - Ultimate Bots Vitaly Bulatov Xenia Bulatov NVIDIA Robotics To learn more about Solo Tech at the frontier of Physical AI: #PhysicalAI #Robotics #Humanoids #NVIDIA #AGIBOT #SoloTech #EmbodiedAI #SoloSeven

Dhruv Diddi

26,741 views • 5 months ago

#AGIBOTAIWeek Day 3: Introducing GO-2: the next-generation foundation model for embodied AI, built to unify reasoning and action. To truly bridge “thinking” and “doing,” embodied AI must solve two challenges at once: • It generate executable action plans through deep spatial reasoning • It deliver stable execution in real-world environments GO-2 tackles both with a comprehensive architecture: Action Chain-of-Thought for action reasoning, and an Asynchronous Dual-System for robust execution. #AGIBOT #AGIBOTAIWeek #Foundation #model #EmbodiedAI Official account tags X: AGIBOT LinkedIn: agibot

#AGIBOTAIWeek Day 3: Introducing GO-2: the next-generation foundation model for embodied AI, built to unify reasoning and action. To truly bridge “thinking” and “doing,” embodied AI must solve two challenges at once: • It generate executable action plans through deep spatial reasoning • It deliver stable execution in real-world environments GO-2 tackles both with a comprehensive architecture: Action Chain-of-Thought for action reasoning, and an Asynchronous Dual-System for robust execution. #AGIBOT #AGIBOTAIWeek #Foundation #model #EmbodiedAI Official account tags X: AGIBOT LinkedIn: agibot

Hasan Toor

36,202 views • 6 months ago

The humanoid robot Lingxi X2 can easily dodge thrown balls… powered by AGIBOT’s newly launched AGILE perception-motion foundation model. With this motion intelligence, it can avoid interference and make real-time perceptual adjustments when working in complex environments. I’m curious whether it could dodge a human’s flying kick? (They always seem to kick humanoid robots during testing.)

The humanoid robot Lingxi X2 can easily dodge thrown balls… powered by AGIBOT’s newly launched AGILE perception-motion foundation model. With this motion intelligence, it can avoid interference and make real-time perceptual adjustments when working in complex environments. I’m curious whether it could dodge a human’s flying kick? (They always seem to kick humanoid robots during testing.)

CyberRobo

32,796 views • 4 months ago

AGIBOT just dominated its debut at the 2nd World Humanoid Robot Games in Beijing. The company finished #1 in both the gold medal table and the overall medal table: • 18 gold medals • 16 silver medals • 12 bronze medals • 46 medals total What stands out is the hardware behind those results. AGIBOT says its competition lineup, including OmniHand, G2, A3 and X2, consists of robots already in mass production or deployed in real-world applications, rather than machines built only for competition. Some of the strongest results: • OmniHand reached all 8 dexterous-hand finals and won 7 gold medals, including Block Building, Powder Weighing and Bean Picking with Tweezers. • AGIBOT A3 won gold in Tai Chi, testing whole-body coordination, balance and controlled posture transitions. • AGIBOT X2 took gold in the obstacle race. • AGIBOT also won 5 gold medals across scenario-based tasks including hotel services, library operations and emergency response. • AGIBOT G2, used during the Games, has already been deployed in factories operated by companies including Longcheer and SAIC. AGIBOT also announced in June 2026 that its 15,000th robot had rolled off the production line. The World Humanoid Robot Games are becoming much more than a showcase of running and dancing robots. Dexterous manipulation, industrial tasks, service work and emergency-response scenarios are increasingly part of the competition. For AGIBOT, 46 medals provide another public test of the same robotic platforms it is trying to move from demonstrations into real-world deployment.

AGIBOT just dominated its debut at the 2nd World Humanoid Robot Games in Beijing. The company finished #1 in both the gold medal table and the overall medal table: • 18 gold medals • 16 silver medals • 12 bronze medals • 46 medals total What stands out is the hardware behind those results. AGIBOT says its competition lineup, including OmniHand, G2, A3 and X2, consists of robots already in mass production or deployed in real-world applications, rather than machines built only for competition. Some of the strongest results: • OmniHand reached all 8 dexterous-hand finals and won 7 gold medals, including Block Building, Powder Weighing and Bean Picking with Tweezers. • AGIBOT A3 won gold in Tai Chi, testing whole-body coordination, balance and controlled posture transitions. • AGIBOT X2 took gold in the obstacle race. • AGIBOT also won 5 gold medals across scenario-based tasks including hotel services, library operations and emergency response. • AGIBOT G2, used during the Games, has already been deployed in factories operated by companies including Longcheer and SAIC. AGIBOT also announced in June 2026 that its 15,000th robot had rolled off the production line. The World Humanoid Robot Games are becoming much more than a showcase of running and dancing robots. Dexterous manipulation, industrial tasks, service work and emergency-response scenarios are increasingly part of the competition. For AGIBOT, 46 medals provide another public test of the same robotic platforms it is trying to move from demonstrations into real-world deployment.

Techniahqrobot | humanoid robots

14,869 views • 1 month ago

📣 Microsoft Research releases Florence-VL, a new family of MLLMs powered by the generative vision foundation model Florence-2. Achieves significant improvements in general VQA, perception, hallucination, OCR, Chart, knowledge-intensive understanding, and more🔥Learn more👇

📣 Microsoft Research releases Florence-VL, a new family of MLLMs powered by the generative vision foundation model Florence-2. Achieves significant improvements in general VQA, perception, hallucination, OCR, Chart, knowledge-intensive understanding, and more🔥Learn more👇

Gradio

14,454 views • 1 year ago

In their latest video, Boston Dynamics’s AI team explains how they make the Atlas humanoid perceive and interact with the world. Atlas uses an agile perception system to understand both the shape and context of objects in complex environments. Atlas combines 2D and 3D awareness, keypoint-based localization, and an object pose tracking system that fuses vision, kinematics, and object knowledge to handle occlusion and uncertainty. Accurate calibration ensures precise hand–eye coordination for reliable manipulation. The team is now working toward a unified model that merges perception and control – pushing beyond spatial AI toward physical intelligence.

In their latest video, Boston Dynamics’s AI team explains how they make the Atlas humanoid perceive and interact with the world. Atlas uses an agile perception system to understand both the shape and context of objects in complex environments. Atlas combines 2D and 3D awareness, keypoint-based localization, and an object pose tracking system that fuses vision, kinematics, and object knowledge to handle occlusion and uncertainty. Accurate calibration ensures precise hand–eye coordination for reliable manipulation. The team is now working toward a unified model that merges perception and control – pushing beyond spatial AI toward physical intelligence.

The Humanoid Hub

50,160 views • 1 year ago

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.

A new member joins the European humanoid robot team. German company Agile Robotics has launched its first industrial humanoid robot, Agile One, featuring intuitive human-robot interaction, dexterous hands (for grasping small screws and touching the screen), and AI-driven operation trained in the real world. It performs tasks such as material collection, handling, picking and placing, loading and unloading machine tools, tool handling, and precision operations. The emergence of humanoid robots by industrial robot companies is a natural progression; they are designed for areas inaccessible to traditional robotic arms and industrial robots, extending operational systems and enhancing business collaboration capabilities. Agile One will be manufactured in Germany in early 2026 and deployed for on-site training at customers to enhance its AI model capabilities. Soon, Europe will enter its second wave of automation driven by intelligent technology.

CyberRobo

72,567 views • 10 months ago

Booster T2 is now available. Smart. Powerful. Built to Perform. As a new flagship embodied development platform, Booster T2 delivers up to 2070 TFLOPS of computing power, the highest in its class for bipedal humanoid robots. Combined with a high-output, high-DOF body, it enables sharper motion perception, stronger understanding, and reliable execution in complex scenarios. From real-time perception to rapid response, from agile movement to precise manipulation, Booster T2 combines powerful computing performance and reliable hardware with Booster Studio's integrated development platform, unlocking limitless possibilities for developers. Search "Booster Robotics" to visit the official website and order now.

Booster T2 is now available. Smart. Powerful. Built to Perform. As a new flagship embodied development platform, Booster T2 delivers up to 2070 TFLOPS of computing power, the highest in its class for bipedal humanoid robots. Combined with a high-output, high-DOF body, it enables sharper motion perception, stronger understanding, and reliable execution in complex scenarios. From real-time perception to rapid response, from agile movement to precise manipulation, Booster T2 combines powerful computing performance and reliable hardware with Booster Studio's integrated development platform, unlocking limitless possibilities for developers. Search "Booster Robotics" to visit the official website and order now.

Booster Robotics

1,139,699 views • 3 months ago

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?

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?

Lukas Ziegler

61,610 views • 1 year ago

NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

Léo

23,248 views • 23 days ago

We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim Massachusetts Institute of Technology (MIT). We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency! PDF: Video: We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes. This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods. Kudos to Yunlong Song! UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) Massachusetts Institute of Technology (MIT)MechE

We are excited to share our #CORL2024 paper (oral) on "Learning Quadruped Locomotion Using Differentiable Simulation" done in collaboration with Sangbae Kim Massachusetts Institute of Technology (MIT). We present a new way to learn to walk in minutes without parallelization, outperforming PPO in sample efficiency! PDF: Video: We present a new framework for learning quadruped locomotion. By leveraging differentiable simulation for policy optimization, our approach achieves fast convergence and stable training, significantly outperforming model-free #ReinforcementLearning methods like PPO in sample efficiency. The key enabler is to combine a high-fidelity, non-differentiable simulator for forward dynamics with a simplified surrogate model for gradient backpropagation. Our framework enables learning quadruped walking in simulation in minutes without parallelization. When augmented with GPU parallelization, our approach allows the quadruped robot to master diverse locomotion skills on challenging terrains in minutes. This work highlights one of the first successful real-world applications of differentiable simulation for quadruped robots, offering a compelling alternative to traditional RL methods. Kudos to Yunlong Song! UZH Science University of Zurich UZH Space Hub UZH IfI European Research Council (ERC) Massachusetts Institute of Technology (MIT)MechE

Davide Scaramuzza

15,572 views • 1 year ago

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

The TWIML AI Podcast

22,592 views • 9 months ago

A robot that sees the terrain and predicts its own future… up to 5 seconds ahead? This is real. ❗️Best Systems Paper finalist at #RSS2025 The team introduces a perceptive Forward Dynamics Model that helps legged robots safely navigate rough, complex environments: no manual tuning no rigid-body limits. Why this matters ✅ Predicts future robot states using both perception and proprioception ✅ Trained on years of sim + real data for strong sim-to-real performance ✅ Enables zero-shot navigation with minimal cost tuning ✅ Boosts success rates and safety on rough terrain like never before This isn’t just another planner, it’s a big step toward robust, adaptable locomotion. Paper: Project website:

A robot that sees the terrain and predicts its own future… up to 5 seconds ahead? This is real. ❗️Best Systems Paper finalist at #RSS2025 The team introduces a perceptive Forward Dynamics Model that helps legged robots safely navigate rough, complex environments: no manual tuning no rigid-body limits. Why this matters ✅ Predicts future robot states using both perception and proprioception ✅ Trained on years of sim + real data for strong sim-to-real performance ✅ Enables zero-shot navigation with minimal cost tuning ✅ Boosts success rates and safety on rough terrain like never before This isn’t just another planner, it’s a big step toward robust, adaptable locomotion. Paper: Project website:

Ilir Aliu

43,990 views • 1 year ago

#AGIBOTAIWeek Day4: Introducing GE-Sim 2.0 (Genie Envisioner World Simulator 2.0) , a comprehensive embodied world simulator for robotic manipulation. By integrating future video generation, proprioceptive state estimation, and reward-based policy assessment, GE-Sim 2.0 moves beyond visual simulation toward an embodied world simulator with built-in evaluation capability, laying the foundation for a world-model-centric platform for closed-loop and interactive policy learning. It provides a practical framework for scalable evaluation and training of robotic manipulation policies. Project page: #AGIBOT #AGIBOTAIWeek #GenieEnvisioner #Robotics #visualsimulation

#AGIBOTAIWeek Day4: Introducing GE-Sim 2.0 (Genie Envisioner World Simulator 2.0) , a comprehensive embodied world simulator for robotic manipulation. By integrating future video generation, proprioceptive state estimation, and reward-based policy assessment, GE-Sim 2.0 moves beyond visual simulation toward an embodied world simulator with built-in evaluation capability, laying the foundation for a world-model-centric platform for closed-loop and interactive policy learning. It provides a practical framework for scalable evaluation and training of robotic manipulation policies. Project page: #AGIBOT #AGIBOTAIWeek #GenieEnvisioner #Robotics #visualsimulation

AGIBOT

28,219 views • 6 months ago

𝗖𝗵𝗶𝗻𝗮 𝗶𝘀 𝗳𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗿𝗼𝗯𝗼𝘁 𝗿𝗮𝗰𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝘀 𝗶𝘁 𝗵𝗮𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. AGIBOT held its Partner Conference in Shanghai last week. The real headline wasn't the new hardware. It was their CTO standing on stage, telling investors that humanoid R&D season is over. 2026, he said, is "Deployment Year One." Not research. Not demos. Deployment into real factories, real warehouses, real stores. The manufacturing ramp is getting faster. 1,000 humanoid robots in the first 2 years. Another 4,000 in the next 12 months. Another 5,000 in just 3 months after that. AGIBOT is now shipping more humanoids per quarter than most US robotics companies have built in their entire existence. Then came the announcements the industry will spend the rest of the year reacting to. AIMA. The first full-stack open architecture for embodied AI. A unified robot operating system called Link-U, three dev platforms for motion, interaction, and task creation, plus an open agent framework. Any developer can build on top of it. This is the Android play for humanoids. GO-2. A vision-language-action foundation model with Action Chain-of-Thought reasoning. Planning and execution collapsed into one model. GE-2. A world model for simulation, strategy testing, and sim-to-real transfer. AGIBOT WORLD 2026. An open-source, production-grade real-world dataset pulled from actual industrial, logistics, hotel, and commercial sites. Seven standardized "productivity packages" covering logistics sorting, retail service, security patrol, commercial cleaning, and more. Plug, deploy, bill. A 5-year, $280 million commitment to seed a global developer and partner ecosystem. Now look at the competition. Boston Dynamics has been building humanoids since 1992. Tesla's Optimus is still climbing its own hype curve. Apptronik and Agility are well-funded but pre-scale on real deployments. AGIBOT has pulled all of this off in three years, with no acquisitions, no legacy platform, and no IPO distractions. While the West is still asking when humanoids will scale, China is already shipping them by the thousand.

𝗖𝗵𝗶𝗻𝗮 𝗶𝘀 𝗳𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗿𝗼𝗯𝗼𝘁 𝗿𝗮𝗰𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝘀 𝗶𝘁 𝗵𝗮𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. AGIBOT held its Partner Conference in Shanghai last week. The real headline wasn't the new hardware. It was their CTO standing on stage, telling investors that humanoid R&D season is over. 2026, he said, is "Deployment Year One." Not research. Not demos. Deployment into real factories, real warehouses, real stores. The manufacturing ramp is getting faster. 1,000 humanoid robots in the first 2 years. Another 4,000 in the next 12 months. Another 5,000 in just 3 months after that. AGIBOT is now shipping more humanoids per quarter than most US robotics companies have built in their entire existence. Then came the announcements the industry will spend the rest of the year reacting to. AIMA. The first full-stack open architecture for embodied AI. A unified robot operating system called Link-U, three dev platforms for motion, interaction, and task creation, plus an open agent framework. Any developer can build on top of it. This is the Android play for humanoids. GO-2. A vision-language-action foundation model with Action Chain-of-Thought reasoning. Planning and execution collapsed into one model. GE-2. A world model for simulation, strategy testing, and sim-to-real transfer. AGIBOT WORLD 2026. An open-source, production-grade real-world dataset pulled from actual industrial, logistics, hotel, and commercial sites. Seven standardized "productivity packages" covering logistics sorting, retail service, security patrol, commercial cleaning, and more. Plug, deploy, bill. A 5-year, $280 million commitment to seed a global developer and partner ecosystem. Now look at the competition. Boston Dynamics has been building humanoids since 1992. Tesla's Optimus is still climbing its own hype curve. Apptronik and Agility are well-funded but pre-scale on real deployments. AGIBOT has pulled all of this off in three years, with no acquisitions, no legacy platform, and no IPO distractions. While the West is still asking when humanoids will scale, China is already shipping them by the thousand.

Shruti

214,939 views • 5 months ago

Researchers at the National University of Singapore have created a new underwater robot inspired by one of the ocean’s most intelligent creatures—the octopus. Designed for both practical underwater tasks and biomechanical research, the robot mimics the octopus’s unique swimming and movement abilities. “Our research journey began long ago with studying diverse principles for reaching and grasping, locomotion, and swimming,” said Prof. Cecilia Laschi, who led the study. Unlike many marine animals, octopuses are exceptionally agile—they can swim swiftly and interact with objects around them. The goal of the study was to develop a robotic system that closely replicates these advanced underwater capabilities. Credit: NUS Soft Robotics Lab #engineering #technology #robots #robotics #softrobotics -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.

Researchers at the National University of Singapore have created a new underwater robot inspired by one of the ocean’s most intelligent creatures—the octopus. Designed for both practical underwater tasks and biomechanical research, the robot mimics the octopus’s unique swimming and movement abilities. “Our research journey began long ago with studying diverse principles for reaching and grasping, locomotion, and swimming,” said Prof. Cecilia Laschi, who led the study. Unlike many marine animals, octopuses are exceptionally agile—they can swim swiftly and interact with objects around them. The goal of the study was to develop a robotic system that closely replicates these advanced underwater capabilities. Credit: NUS Soft Robotics Lab #engineering #technology #robots #robotics #softrobotics -------------------------------- Stay ahead of the curve! Follow us now on our WhatsApp ( and Telegram ( channels and stay updated about the cutting edge.

Wevolver

47,573 views • 1 year ago