${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
arXiv cs.LG / 4/20/2026
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Key Points
- The paper introduces pi0.7, a steerable robotic foundation model designed to perform well out of the box across many scenarios without needing task-specific retraining.
- pi0.7 can follow complex language instructions in unseen environments, including multi-stage kitchen tasks, and it demonstrates zero-shot cross-embodiment generalization (e.g., folding laundry without prior exposure).
- The model matches the performance of more specialized reinforcement-learning fine-tuned models on challenging tasks such as operating an espresso machine in a zero-shot setting.
- Its core approach is “diverse context conditioning” during training, where prompts include not only language goals but additional multimodal steering signals (e.g., performance metadata and subgoal images) that encode strategies.
- Training leverages a wide range of data sources, including demonstrations, possibly suboptimal autonomous/failure data, and data collected outside of robotics, and is evaluated across multiple robot platforms and task types.
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