ZDTaichu5.0-9B is a 9B edge-deployable multimodal model built on a Qwen3.5 backbone with C-RADIOv4 vision encoder that leads open sub-10B VLMs on spatial-reasoning benchmarks including ViewSpatial and MMSI-Bench, while supporting embodied AI and tool-use agent tasks. Its strong spatial reasoning performance at edge scale makes it particularly relevant for on-device robot perception.

Light-Loco-Parkour presents a multi-skill distillation approach for learning perceptive whole-body parkour and locomotion skills deployable on real robots, enabling versatile agile movement across diverse terrains. The method advances embodied locomotion by combining perception and whole-body control into a unified distilled policy.

OM-1 is a robot foundation model trained exclusively on human manipulation data (no teleoperation or robot demonstrations) that achieves near human-level dexterity and zero-shot generalization across tabletop arms, industrial arms, and humanoids, including multi-robot collaboration. Its ability to transfer from human video data alone to diverse robot morphologies marks a notable step toward scalable robot learning.

Uses discrete semantic actions paired with embodiment-specific interpreters to turn any pretrained VLM into a robot controller without a dedicated policy network or additional pretraining. Demonstrates strong zero-shot generalization across diverse tasks and environments.

Renders point clouds as three orthographic 2D views fed into a pretrained PaliGemma VLM, predicting heatmaps to recover 6-DoF actions with temporal/spatial memory modules. Achieves 95.4% success on 13 real Franka tasks with only 3 demos per task, outperforming π0, RVT-2, and 3D Diffuser Actor, with fully open weights and code.

Reinforcement learning (RL) has become a powerful tool for quadrupedal locomotion, and a sim-to-real approach is widely adopted to avoid hardware damage during training. However, the sim-to-real gap remains a significant challenge.

Robotic grasping in cluttered scenes is dominated by methods that optimise either stability or a predefined task category, leaving a gap between 'can this grasp hold the object' and 'can this grasp serve the task'. This paper bridges that gap using foundation model semantic embeddings.

HiDream-O1-Embodied is a unified native architecture that ingests text, images, and video and directly outputs physical actions, positioning itself as a step beyond passive world modeling toward active embodied interaction. The architecture aims to close the loop between perception and action within a single model framework.

Axis introduced a Real2Sim2Real pipeline that converts real objects and scenes into high-fidelity physics-accurate simulations and validates learned policies back on hardware, paired with a performance-driven active trajectory curation engine. This data flywheel approach targets improved robot learning policy quality by systematically closing the sim-to-real gap.

GPT-6 Astra is demonstrated solving a Rubik's Cube using dexterous robot hands in a real physical setup, showcasing a significant leap in vision-language-action integration for fine-grained dexterous manipulation. This demo highlights the frontier of embodied AI combining large foundation models with real-world dexterity.