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FP3: A 3D Foundation Policy for Robotic Manipulation
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Following its success in natural language processing and computer vision, foundation models that are pre-trained on large-scale multi-task datasets have also shown great potential in robotics. However, most existing robot foundation models rely solely on 2D image observations, ignoring 3D geometric information, which is essential for robots to perceive and reason about the 3D world. In this paper, we introduce FP3, a first large-scale 3D foundation policy model for robotic manipulation. FP3 builds on a scalable diffusion transformer architecture and is pre-trained on 60k trajectories with point cloud observations. With the model design and diverse pre-training data, FP3 can be efficiently fine-tuned for downstream tasks while exhibiting strong generalization capabilities. Experiments on real robots demonstrate that with only 80 demonstrations, FP3 is able to learn a new task with over 90% success rates in novel environments with unseen objects, significantly surpassing existing robot foundation models.
Forward citations
Cited by 4 Pith papers
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BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation
Adding stage-wise temporal and spatial memory to a heatmap-prediction 3D VLA policy yields strong results on memory-dependent manipulation benchmarks while keeping data efficiency.
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See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models
Expressing RGB-D observations as end-effector-centered robot-frame pointmaps and adding them element-wise to RGB tokens improves pretrained VLAs under camera viewpoint variation.
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StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
A vision-language-action model that fuses stereo-derived geometric features with semantic features improves real-world grasping success and camera-pose robustness over single-view baselines.
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QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models
Adding an auxiliary quantized-depth-token prediction task to a VLA policy improves manipulation success rates on LIBERO, Simpler, and real-robot pick-and-place tasks versus the open-pi-zero baseline.
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