用能量函数优化动作序列，让模型更准达成目标。
Latent Energy Action Planning with World Models
- 把整个动作序列当作可微变量，联合优化终点状态与目标一致
- 在四个控制任务中成功率从77.5%提升至94.8%，提升17.3个百分点
- 适合需要高精度规划的机器人控制场景，保持原有模型不变
隐空间世界模型支持从高维观测中高效进行模型预测控制，但优化单一学习到的隐空间目标可能导致解码器预测的终态描述符与目标描述符不匹配。本文提出隐能量动作规划（LEAP），将完整动作时序视为可微变量，并通过冻结的LeWorldModel（LeWM）进行优化。LEAP将终态隐空间匹配与终态窗口状态能量耦合：低能量要求预测的终态隐空间与目标隐空间一致，且解码器预测的终态描述符与目标描述符一致。采用目标条件化的初始提案启动搜索，通过自回归推演使用拟牛顿法精炼动作，最后通过投影操作约束动作范围可接受性。在官方发布的四个控制领域使用LeWM检查点评估，完整LEAP系统在相同协议下将平均成功率从LeWM+CEM的77.5%提升至94.8%，提升17.3个百分点，同时保留冻结的LeWM表示与预测能力。
原文摘要 · Abstract (English)
Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can favor action sequences whose decoder-predicted terminal descriptor does not match the goal descriptor. We introduce Latent Energy Action Planning (LEAP), which treats the complete action horizon as a differentiable variable and optimizes it through a frozen LeWorldModel (LeWM). LEAP couples terminal latent goal matching with a terminal-window state energy. Low energy requires the predicted terminal latent to agree with the goal latent and the decoder-predicted terminal descriptor to agree with the goal descriptor. A frozen goal-conditioned proposal initializes the search, a quasi-Newton solver refines actions through the autoregressive rollout, and post-optimization projection enforces the admissible action range. Across four control domains using the officially released LeWM checkpoints, the complete LEAP planning system raises mean success from 77.5% for LeWM planned with the cross-entropy method (LeWM+CEM) to 94.8% under a matched protocol, a 17.3-percentage-point improvement, while retaining the frozen LeWM representation and predictor.
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