发表机构
Kyoto University; Fujitsu Limited（京都大学; 富士通株式会社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对潜在世界模型规划中潜在状态误差导致决策失败的问题，提出将成功准则量作为辅助训练目标，仅此损失即在两个任务上显著提升成功率，表明成功准则可直接作为训练目标。
AI 中文摘要
潜在世界模型通过在潜在空间中用距离对候选动作序列进行评分来规划。然而，任务成功是由物理量来判定的，我们称之为成功准则量。在我们检查的所有四个潜在世界模型中，末端执行器位置被编码到潜在状态中，其误差大于成功准则所允许的范围。这样的潜在状态无法区分成功的候选与失败的候选。我们提出了一种辅助损失，将成功准则量作为训练目标，而现有的潜在世界模型仅将它们作为输入。在训练期间，编码器和预测器输出上的线性头对成功准则量进行回归，并将回归误差添加到训练损失中。训练后该线性头被丢弃，因此模型、其成本及其测试时的输入保持不变。仅此损失就在PushT和cube任务上分别将成功率提高了3.5%和3.4%（绝对值），且两项改进在统计上均显著。因此，成功准则规定了世界模型必须在潜在状态中保留什么，我们表明它可以直接用作训练目标。
英文摘要
Latent world models plan by scoring candidate action sequences with distances in latent space. However, task success is judged by physical quantities, which we call the success-criterion quantities. In all four latent world models we examine, the end-effector position is encoded in the latent state with an error larger than the success criterion allows. Such a latent state cannot separate successful candidates from failing ones. We propose an auxiliary loss that uses success-criterion quantities as training targets, whereas existing latent world models take them only as inputs. During training, a linear head on the encoder and predictor outputs regresses the success-criterion quantities, and the regression error is added to the training loss. The head is discarded after training, so the model, its cost, and its inputs at test time are unchanged. This loss alone improves the success rate on PushT and cube by 3.5% and 3.4% (absolute), respectively, and both improvements are statistically significant. A success criterion thus specifies what a world model must retain in its latent state, and we show that it can serve directly as a training target.
Comments21 pages, 6 figures, 10 tables. Under review
