ACE Robotics
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ACE Robotics

Empowering Every Robots With A Super- Brain
ACE Robotics releases ACE-Brain-0.6, says it outperforms GPT-6 Astra on spatial grounding - RobotHOT

ACE Robotics releases ACE-Brain-0.6, says it outperforms GPT-6 Astra on spatial grounding

ACE Robotics has released a visual-spatial grounding-enhanced embodied foundation model, ACE-Brain-0.6, and said the 9-billion-parameter system outperformed OpenAI’s GPT-6 Astra on six of eight grounding benchmarks. The company said the model is designed to improve target identification, spatial localization and interaction anchoring—capabilities it described as critical for translating high-level task reasoning into reliable physical action. It builds on the earlier ACE-Brain-0.5 and focuses on visual grounding: identifying target objects, interaction areas and spatial references, and linking them to position, displacement and motion parameters. In seven public benchmarks, ACE-Brain-0.6 averaged 74.53 points versus 73.68 for GPT-6 Astra, according to the company. It said the model beat GPT-6 Astra on six of eight evaluations, including Where2Place (76.55 vs. 71.10), PixMo-Count (89.41 vs....

Embodied AI
ACE ROBOTICS open-sources ACE-Ego-Hand for 3D hand reconstruction from egocentric video, claims 45 benchmark wins - RobotHOT

ACE ROBOTICS open-sources ACE-Ego-Hand for 3D hand reconstruction from egocentric video, claims 45 benchmark wins

ACE ROBOTICS, in collaboration with the Chinese University of Hong Kong’s Multimedia Laboratory and Nanyang Technological University, has open-sourced ACE-Ego-Hand, a 3D dual-hand shape reconstruction model designed for first-person video, generating approximately 5,000 hours of training data that could serve as a foundational dataset for embodied AI model training. The model extracts structured 3D bimanual manipulation trajectories from massive egocentric video footage, translating human grasping, assembly and hand-coordination actions into machine-learnable data. It marks the first time a video generation model has been repurposed as a DreamHand encoder for end-to-end training, outputting hand pose, shape and 3D position in a single forward pass without frame-by-frame detection or temporal regression. Efficiency gains over traditional approaches Unlike previous hand models reliant on...

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