BridgeVLA++: Keep the Geometry, Give the Robot a Memory
from Embodied AI 101 Hosted by Shaoqing Tan
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. In this episode of Embodied AI 101, we explore "BridgeVLA++: Keep the Geometry, Give the Robot a Memory". We break down the research, methodology, and real-world implications for robotics, AI, and physical intelligence. Embodied AI 101 covers the latest research at the intersection of AI and physical intelligence — robotics, manipulation, world models, and the path from digital intelligence to embodied agents.
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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.
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