Source note
π 0.7 π_{0.7} : a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Vision Language ActionRobot Foundation ModelGeneralist Robot PolicyCross Embodiment TransferRobot Data Scaling
Summary
is a 5B-parameter generalist vision-language-action model for robot control that uses richer prompts to learn from mixed-quality, multi-source data. The paper claims this prompt design gives stronger zero-shot generalization across tasks, environments, and robot embodiments, with out-of-the-box performance on dexterous long-horizon tasks.
Problem
- Prior robot foundation models struggle with compositional generalization: they often fail to combine learned skills for new tasks and may need task-specific fine-tuning even on tasks close to training data.
- Training on large, diverse robot data is hard because trajectories differ in strategy and quality; if trained naively, the model can average across good and bad behaviors and produce weak actions.
- This matters because useful robot foundation models need to work in unseen homes, follow varied language instructions, transfer across robots, and benefit from failures, autonomous rollouts, and non-robot data instead of discarding them.
Approach
- The core idea is to condition the robot policy on a richer prompt that says both what to do and how to do it. The prompt can include a task instruction, a step-level subtask instruction, episode metadata, control mode, and subgoal images.
- Episode metadata labels each trajectory with attributes such as speed, quality score from 1 to 5, and whether a segment contains a mistake. This lets the model train on failures, suboptimal demonstrations, and autonomous rollouts without treating them as targets to imitate blindly.
- Subgoal images show a desired near-future scene state. A separate world model, initialized from BAGEL, generates these images from the current observation and subtask text, giving the action model a more concrete target than language alone.
- The base policy is a flow-matching VLA built on a Gemma3 4B vision-language backbone, MEM-style video history encoding, and an 860M action expert, for about 5B parameters total.
- During training, prompt parts are randomly dropped so the model can run with any subset at test time. Subgoal images are used on 25% of training examples; metadata is dropped entirely 15% of the time; individual metadata fields are dropped with 5% probability.
Results
- The model has about 5B parameters: a 4B Gemma3 VLM backbone plus an 860M action expert.
- The paper claims strong out-of-the-box performance on dexterous long-horizon tasks such as operating an espresso machine, folding laundry, taking out a trash bag, folding a box, and peeling vegetables, without task-specific post-training.
- It claims zero-shot cross-embodiment transfer, including t-shirt folding on a robot with no laundry-folding training, and states this matches expert teleoperators on their initial attempts.
- It claims instruction following in unseen kitchen and bedroom environments and compositional generalization to new tasks such as loading a sweet potato into an air fryer.
- The abstract states espresso-machine performance matches specialized RL-finetuned models, and the introduction says the system can sometimes outperform RL-trained or single-task post-trained policies on dexterous tasks by learning from diverse and suboptimal data.
- The provided excerpt does not include task tables, success rates, or benchmark numbers, so the quantitative evidence available here is limited to model and training setup numbers rather than evaluation metrics.