SymVD：面向机器人操作的对称视觉语言动作蒸馏
SymVD: Symmetric Vision Language Action Distillation for Robot Manipulation
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SymVD是利用操作任务几何对称性的VLA蒸馏框架，采用等变actor-critic架构与自适应加权方案，提升了机器人操作任务的样本效率与泛化能力，优于标准蒸馏及SAC。
AI中文摘要：
尽管预训练的视觉语言动作（Vision-Language-Action, VLA）模型在机器人操作任务中具备广泛的泛化能力，但将其适配到真实环境或处理任务偏移时，往往需要大量额外数据与重新训练。为解决这一问题，我们提出对称VLA蒸馏（Symmetric VLA Distillation, SymVD）框架，该框架通过显式利用操作任务中的几何对称性（如旋转与反射不变性），将大型VLA教师模型的知识迁移至紧凑的学生策略。SymVD采用等变actor-critic架构，并使用与教师动作在群不变属性下对齐的对称感知目标函数训练学生。我们证明，通过强制策略遵循等变性，SymVD减少了群变换相关配置间的冗余探索，并提升了蒸馏过程中的样本效率。为进一步稳定与优化蒸馏，SymVD引入自适应加权方案，该方案基于训练进度动态平衡蒸馏目标与强化学习更新，即使教师信号存在缺陷或未对齐，也能实现鲁棒迁移。在机器人操作任务上的实验结果表明，SymVD相较于标准蒸馏方法表现始终更优，且在样本效率与对环境中先前未见过的对称变换的泛化能力上优于SAC。
英文摘要：
While pretrained Vision-Language-Action (VLA) models offer broad generalization capabilities in robotic manipulation tasks, adapting them to real-world environments or handling task shifts often requires substantial additional data and retraining. To address this, we propose Symmetric VLA Distillation (SymVD), a distillation framework that transfers knowledge from a large VLA teacher to a compact student policy by explicitly exploiting geometric symmetries in manipulation tasks, such as rotational and reflectional invariance. SymVD employs an equivariant actor-critic architecture and trains the student using a symmetry-aware objective that aligns with teacher actions under group-invariant properties. We demonstrate that by enforcing the policy to respect equivariance, SymVD reduces redundant exploration across configurations related by group transformations and improves sample efficiency during distillation. To further stabilize and improve distillation, SymVD introduces an adaptive weighting scheme that dynamically balances the distillation objective and reinforcement learning updates based on training progress, enabling robust transfer even when the teacher signal is imperfect or misaligned. Experimental results on robotic manipulation tasks demonstrate that SymVD consistently improves over standard distillation and also outperforms SAC in terms of sample efficiency and generalization to previously unseen symmetric transformations of the environment.
