发表机构
National University of Defense Technology; Shanghai AI Laboratory（国防科技大学; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出LCAM剪枝方法，通过损失条件激活矩和自递归粗到细流程，无需训练即可高效剪枝机器人操作策略，在LIBERO和OpenVLA上保持高性能，并验证于真实机器人乒乓球任务。
AI 中文摘要
网络剪枝可以减少机器人策略中的参数冗余。然而，通用的剪枝标准是为图像识别任务量身定制的，通常设计用于保持权重幅度、局部重建或语言模型似然性，而非闭环动作行为。直接将这类剪枝算法应用于机器人任务会产生不令人满意的性能。在本文中，我们提出了损失条件激活矩（LCAM）剪枝，一种无需训练的预训练机器人操作策略非结构化剪枝方法。具体而言，我们首先使用行归一化权重贡献、在校准演示上测量的激活矩以及输出方向对动作预测损失的敏感性对连接进行排序。我们进一步设计了一种自递归的从粗到细流程：在每次嵌套的粗剪枝阶段后重新校准重要性，而在稀疏度拐点之后，留出的离线动作失真指导细粒度预算分配。我们的算法在剪枝后无需昂贵的恢复训练和模拟器回放。在三个LIBERO套件上使用竞争性机器人策略进行的实验，以及对OpenVLA的评估，表明LCAM在广泛的剪枝比率下实现了竞争性性能。值得注意的是，在LIBERO-Object上使用OpenVLA，我们的LCAM在50%非结构化剪枝下实现了84.0%的成功率，保留了密集策略超过90%的成功率。在真实世界机器人乒乓球上的有前景的结果进一步证明了我们剪枝算法的有效性。
英文摘要
Network pruning can reduce parameter redundancy in robotic policies. However, generic pruning criteria are tailored for image recognition tasks and commonly designed to preserve weight magnitude, local reconstruction, or language-model likelihood rather than closed-loop action behavior. Directly applying these pruning algorithms to robotic tasks yields unsatisfactory performance. In this paper, we propose Loss-Conditioned Activation-Moment (LCAM) pruning, a training-free method for unstructured pruning of pre-trained robotic manipulation policies. Specifically, we first rank connections using row-normalized weight contribution, activation moments measured on calibration demonstrations, and the sensitivity of output directions to the action-prediction loss. We further design a self-recursive coarse-to-fine procedure: importance is recalibrated after each nested coarse pruning stage, while held-out offline action distortion guides fine-grained budget allocation after a sparsity knee. Our algorithm is free from costly recovery training and simulator rollouts after pruning. Experiments on three LIBERO suites with competitive robotic policies, together with evaluations on OpenVLA, show that LCAM attains competitive performance across a broad range of pruning ratios. Notably, on LIBERO-Object with OpenVLA, our LCAM achieves 84.0% success at 50% unstructured pruning, retaining over 90% of the dense policy's success rate. Promising results on real-world robotic ping pong further demonstrate the effectiveness of our pruning algorithm.
