规划作为下降：在学习的能量景观中进行目标条件化潜在轨迹合成
Planning as Descent: Goal-Conditioned Latent Trajectory Synthesis in Learned Energy Landscapes
AI总结：
PaD通过学习目标条件化能量函数实现离线无奖励规划，以验证为基础进行轨迹合成，取得95%的成功率并优于现有方法。
摘要：
我们提出了规划作为下降（PaD），一种用于离线目标条件强化学习的框架，其将轨迹合成建立在验证基础上。与学习策略或显式规划器不同，PaD在完整的潜在轨迹上学习一个目标条件化能量函数，将低能量分配给可行且目标一致的未来。规划通过在此能量景观中基于梯度的细化来实现，在训练和推理过程中使用相同的计算来减少解耦建模管道中常见的训练-测试不匹配。PaD通过自监督的 hindsight 目标重标记进行训练，塑造能量景观以围绕规划动态。在推理过程中，多个轨迹候选者在不同的时间假设下被细化，低能量计划在可行性和效率之间取得平衡。我们在OGBench立方体操控任务上评估了PaD。当在狭窄专家示范上训练时，PaD实现了95%的成功率，显著优于先前方法在68%峰值处的表现。令人瞩目的是，使用噪声和次优数据训练进一步提高了成功率和计划效率，突显了验证驱动规划的优势。我们的结果表明，学习评估和细化轨迹为离线、无奖励规划提供了一种稳健的替代方法。
英文摘要：
We present Planning as Descent (PaD), a framework for offline goal-conditioned reinforcement learning that grounds trajectory synthesis in verification. Instead of learning a policy or explicit planner, PaD learns a goal-conditioned energy function over entire latent trajectories, assigning low energy to feasible, goal-consistent futures. Planning is realized as gradient-based refinement in this energy landscape, using identical computation during training and inference to reduce train-test mismatch common in decoupled modeling pipelines. PaD is trained via self-supervised hindsight goal relabeling, shaping the energy landscape around the planning dynamics. At inference, multiple trajectory candidates are refined under different temporal hypotheses, and low-energy plans balancing feasibility and efficiency are selected. We evaluate PaD on OGBench cube manipulation tasks. When trained on narrow expert demonstrations, PaD achieves state-of-the-art 95\% success, strongly outperforming prior methods that peak at 68\%. Remarkably, training on noisy, suboptimal data further improves success and plan efficiency, highlighting the benefits of verification-driven planning. Our results suggest learning to evaluate and refine trajectories provides a robust alternative to direct policy learning for offline, reward-free planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
