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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... show more

The Humanoid Hub

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14,020 views • 3 months ago •via X (Twitter)

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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 #Humanoid #AGILE #EmbodiedAI #PhysicalAI

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 #Humanoid #AGILE #EmbodiedAI #PhysicalAI

AGIBOT

24,811 views • 3 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 • 3 months ago

Chinese humanoid robotics company LimX Dynamics has unveiled COSA (Cognitive Operating System of Agents). COSA is described as a unified "brain-body" architecture that allows the robot to think and act simultaneously in the real world. It integrates: - high-level cognition (reasoning, planning, adaptation) - and whole-body motion control (low-latency dynamic locomotion, manipulation) It enables the Oli robot to act on natural language instructions and perform tasks that involve both walking and manipulation while being adaptive to interruptions.

Chinese humanoid robotics company LimX Dynamics has unveiled COSA (Cognitive Operating System of Agents). COSA is described as a unified "brain-body" architecture that allows the robot to think and act simultaneously in the real world. It integrates: - high-level cognition (reasoning, planning, adaptation) - and whole-body motion control (low-latency dynamic locomotion, manipulation) It enables the Oli robot to act on natural language instructions and perform tasks that involve both walking and manipulation while being adaptive to interruptions.

The Humanoid Hub

71,168 views • 8 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 • 3 months ago

Truly general-purpose robots must be able to navigate spaces they have never seen before. ⦿ Skild Brain enables end-to-end autonomous locomotion from raw vision and joint inputs, without mapping or pre-planning. ⦿ The model adapts in real time to new terrain such as stairs, gaps, and clutter, reacting instinctively like a human. There is no “stair mode” or “step-over mode”: the transition between walking behaviors is continuous and instinctive. ⦿ A single neural policy supports a wide range of locomotion tasks and can transfer across different robot platforms.

Truly general-purpose robots must be able to navigate spaces they have never seen before. ⦿ Skild Brain enables end-to-end autonomous locomotion from raw vision and joint inputs, without mapping or pre-planning. ⦿ The model adapts in real time to new terrain such as stairs, gaps, and clutter, reacting instinctively like a human. There is no “stair mode” or “step-over mode”: the transition between walking behaviors is continuous and instinctive. ⦿ A single neural policy supports a wide range of locomotion tasks and can transfer across different robot platforms.

The Humanoid Hub

28,439 views • 1 year ago

The First Manufacturer to Mass-Produce 5,000 Humanoid Robots AgiBOT rolled off its 5,000th humanoid robot (a Lingxi X2) today, marking a milestone for both the company and the humanoid robot industry as a whole: a significant step towards large-scale deployment in the real world. AgiBOT co-founder and CTO Peng Zhihui stated in a live said that these 5,000 units include the Yuanzheng series (1742), the Lingxi series (1846), and the Genie series (1412). These humanoid robots will be deployed in various settings, including scientific research, entertainment, shopping malls, factories, and warehouses. This also signals that AgiBOT has the capacity for mass production and is ready to meet future surges in customer demand. Its production partner, LingyiTech, was the main force behind the assembly of AgiBOT's 5,000 humanoid robots this year, and they are planning an automated production line with an annual capacity of 500,000 units. Next year will be a milestone year for humanoid robots, reaching 10,000 units annually. Including Tesla, 1X, UBTECH…

The First Manufacturer to Mass-Produce 5,000 Humanoid Robots AgiBOT rolled off its 5,000th humanoid robot (a Lingxi X2) today, marking a milestone for both the company and the humanoid robot industry as a whole: a significant step towards large-scale deployment in the real world. AgiBOT co-founder and CTO Peng Zhihui stated in a live said that these 5,000 units include the Yuanzheng series (1742), the Lingxi series (1846), and the Genie series (1412). These humanoid robots will be deployed in various settings, including scientific research, entertainment, shopping malls, factories, and warehouses. This also signals that AgiBOT has the capacity for mass production and is ready to meet future surges in customer demand. Its production partner, LingyiTech, was the main force behind the assembly of AgiBOT's 5,000 humanoid robots this year, and they are planning an automated production line with an annual capacity of 500,000 units. Next year will be a milestone year for humanoid robots, reaching 10,000 units annually. Including Tesla, 1X, UBTECH…

CyberRobo

47,095 views • 9 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 @build Frontier Tower NVIDIA AGIBOT Nebius UFB - Ultimate Fighting 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 @build Frontier Tower NVIDIA AGIBOT Nebius UFB - Ultimate Fighting 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

26,660 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

14,810 views • 16 days ago

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

Galbot

46,103 views • 6 months 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 • 9 months ago

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

Rohan Paul

3,802,543 views • 10 months ago

So cool...a humanoid just drove a go-kart for real. A Unitree G1 lowers itself into the tight cockpit, hands on the wheel, feet on the pedals, then runs the track， even drifts. It shows constrained whole-body loco-manipulation, mobile pick-and-place, and hand-eye-foot coordination in one sequence. This comes from China’s Symbiosis Robotics and their Direct Perception Control (DPC) model. It skips the usual high-level goals + tracker stack, mapping vision, language, body state and feedback straight to joint and hand targets. The focus is whole-body coordination for loco-manipulation. Interestingly, data diversity...Training used 15,010 hours of joint-aligned data-- 6,781h human egocentric, 4,024h armed robots, 3,660h wheeled humanoids, 545h bipedal. Teleop, head-mounted and egocentric sources were all converted into time-aligned joint trajectories across embodiments. A clean test that packs perception, balance and continuous whole-body control into one task chain ,and leaves room for other real-world scenarios.

So cool...a humanoid just drove a go-kart for real. A Unitree G1 lowers itself into the tight cockpit, hands on the wheel, feet on the pedals, then runs the track， even drifts. It shows constrained whole-body loco-manipulation, mobile pick-and-place, and hand-eye-foot coordination in one sequence. This comes from China’s Symbiosis Robotics and their Direct Perception Control (DPC) model. It skips the usual high-level goals + tracker stack, mapping vision, language, body state and feedback straight to joint and hand targets. The focus is whole-body coordination for loco-manipulation. Interestingly, data diversity...Training used 15,010 hours of joint-aligned data-- 6,781h human egocentric, 4,024h armed robots, 3,660h wheeled humanoids, 545h bipedal. Teleop, head-mounted and egocentric sources were all converted into time-aligned joint trajectories across embodiments. A clean test that packs perception, balance and continuous whole-body control into one task chain ,and leaves room for other real-world scenarios.

CyberRobo

76,039 views • 25 days 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 • 8 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

1,138,886 views • 2 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

Agile Continuous Jumping in Discontinuous Terrains discuss: We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.

Agile Continuous Jumping in Discontinuous Terrains discuss: We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.

AK

35,794 views • 2 years 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