EquiKey: Object-Centric SE(3)-Equivariant Keypose Guidance for One-Shot VLA Adaptation

Vision-Language-Action (VLA) models acquire broad semantic and visuomotor priors from large-scale robot data, yet adapting them to novel manipulation tasks remains challenging when only a single demonstration is available. In particular, a generalist VLA may recognize task-relevant objects and reuse familiar motor skills while still failing to infer the precise three-dimensional configurations required for successful execution. We introduce EquiKey, an object-centric SE(3)-equivariant keypose guidance framework for one-shot VLA adaptation. Given a single demonstration of a novel task, a lightweight task-specific SE(3)-equivariant predictor learns to infer a sparse sequence of task-relevant keyposes. These predicted keyposes provide explicit geometric guidance to a pretrained keypose-conditioned EquiKey-VLA, whose backbone remains frozen during task adaptation. During execution, the predicted keypose sequence is temporally scheduled as high-level geometric targets, while EquiKey-VLA generates dense continuous actions conditioned on the latest observation. On 10 one-shot OOD tasks in RoboTwin 2.0, our method achieves average success rates of 77.1% and 76.9% under the Easy and Hard settings, improving the strongest VLA baseline by 42.5% and 44.6% and the strongest task-specific one-shot adaptation baseline by 26.5% and 33.6%, respectively. In real-world one-shot Task-OOD experiments, our method achieves a 73.3% success rate, substantially outperforming the strongest baseline at 11.7%. These results demonstrate that sparse SE(3)-equivariant keypose guidance provides an effective interface for combining geometric inductive biases with the semantic and visuomotor capabilities of pretrained VLA models.