2026 Overseas Humanoid Robot Breakthroughs: Google Full-Body AI, Figure 03 Climbs Ladders, 1X Bionic Hand

2026-09-05 · AI Tech Insights · Curated from AI Robotic Info

Summary: In summer 2026, the overseas humanoid robot field saw intensive technological breakthroughs. Google DeepMind launched Gemini Robotics 2 with full-body control; Figure AI's Figure 03 autonomously climbed industrial ladders; Boston Dynamics' Atlas electric version gained general intelligence; 1X Technologies released a 25-DOF tendon-driven bionic hand; NVIDIA released the open-source SONIC locomotion foundation model. These breakthroughs mark the transition from basic mobility to deep reasoning, multi-limb coordination, and strong environmental adaptability.

Image source: AI Robotic Info

In summer 2026, the overseas humanoid robot field witnessed intensive technological breakthroughs, with multiple tech giants pushing the boundaries of intelligence to unprecedented heights. From Google's full-body decision system to robots that can climb ladders, to ultra-precise bionic fingers, humanoid robots are no longer satisfied with basic mobility but pursuing deep reasoning, multi-limb coordination, and strong environmental adaptability.

Google DeepMind Reshapes Embodied AI: Gemini Robotics 2

Google DeepMind launched Gemini Robotics 2 in early August, featuring full-body control technology. The Apptronik Apollo 2 equipped with this model can coordinate every subtle movement from foot to fingertip. It can autonomously plan routes, bend down to pick up objects, and place them precisely. The model includes a VLA main architecture for task execution and a reasoning module as a high-level cognitive brain. Developers need fewer than 200 training samples and a few hours to adapt new hardware to the system.

Figure AI Breaks Through Mobile Manipulation

Figure AI's founder demonstrated Figure 03 autonomously climbing industrial ladders in a laboratory setting. This is significant because climbing requires visual, upper-limb grasping, and lower-limb support to synchronize within milliseconds. Any minor error causes a fall. The underlying system uses a neural network controller with approximately 10 million parameters, replacing complex hand-written control code.

Boston Dynamics Advances Toward General Intelligence

After fully embracing electric drive systems, Atlas gained a new industry position in summer 2026: genuine autonomy and general intelligence for industrial deployment. The new Atlas adapts to unfamiliar and complex environments, handling a wide range of tasks. The training framework runs the equivalent of millions of hours of virtual simulation training daily, with new skills transferring to the physical robot in about one hour.

1X Technologies: Bionic Dexterous Hand

Norway's 1X Technologies showcased a breakthrough grasping component for NEO robots with 25 degrees of freedom, approaching human limb flexibility. It uses a tendon-driven mechanism with motors hidden inside the arm, pulling finger joints via bionic fibers. This reduces end-effector weight and enables extremely gentle force control with tactile feedback, capable of safely handling fragile items and precisely pinching small coins. The product has entered mass production.

NVIDIA SONIC: Open-Source Locomotion Foundation Model

NVIDIA released the open-source SONIC locomotion foundation model, trained on 700 hours of real human motion capture data. The same control strategy can drive multiple advanced models and even imitate complex martial arts movements in real time, providing researchers with a ready-to-use base that significantly shortens development cycles.

Key Takeaways

• Google DeepMind's Gemini Robotics 2 enables full-body control with fewer than 200 samples
• Figure 03 autonomously climbs industrial ladders, demonstrating mobile manipulation maturity
• Boston Dynamics Atlas electric runs millions of hours of simulation daily, 1-hour skill transfer
• 1X Technologies releases 25-DOF tendon-driven bionic hand, now in mass production
• NVIDIA releases open-source SONIC locomotion model trained on 700 hours of motion capture

📎 This article was automatically compiled by AI from AI Robotic Info (2026-09-03).
All rights belong to the original authors. Used for informational purposes only.