通过潜在空间反向规划的高效机器人策略学习
Efficient Robotic Policy Learning via Latent Space Backward Planning
AI总结：
提出潜在空间反向规划（LBP）方法，通过锚定最终目标并递归预测子目标，实现高效且准确的机器人长时程策略学习，在仿真和真实实验中达到最先进性能。
摘要：
当前的机器人规划方法通常依赖于预测包含完整像素细节的多帧图像。虽然这种细粒度的方法可以作为一种通用的世界模型，但它为下游策略学习带来了两个重大挑战：大量的计算成本阻碍了实时部署，以及累积的不准确性可能误导动作提取。使用粗粒度子目标进行规划部分缓解了效率问题。然而，它们的前向规划方案仍可能因累积误差导致偏离任务预测，从而与长期目标不一致。这引发了一个关键问题：在长时程、多阶段任务中，机器人规划能否既高效又足够准确以支持实时控制？为解决此问题，我们提出了一种潜在空间反向规划方案（LBP），该方案首先将任务锚定到最终潜在目标，然后递归预测更接近当前状态的中间子目标。锚定的最终目标使反向子目标规划始终意识到任务完成情况，从而在整个规划范围内促进任务内预测。子目标条件策略包含一个可学习的标记，用于总结子目标序列，并确定每个子目标如何指导动作提取。通过大量的仿真和真实机器人长时程实验，我们表明LBP优于现有的细粒度和前向规划方法，达到了最先进的性能。项目页面：https://lbp-authors.github.io
英文摘要：
Current robotic planning methods often rely on predicting multi-frame images with full pixel details. While this fine-grained approach can serve as a generic world model, it introduces two significant challenges for downstream policy learning: substantial computational costs that hinder real-time deployment, and accumulated inaccuracies that can mislead action extraction. Planning with coarse-grained subgoals partially alleviates efficiency issues. However, their forward planning schemes can still result in off-task predictions due to accumulation errors, leading to misalignment with long-term goals. This raises a critical question: Can robotic planning be both efficient and accurate enough for real-time control in long-horizon, multi-stage tasks? To address this, we propose a Latent Space Backward Planning scheme (LBP), which begins by grounding the task into final latent goals, followed by recursively predicting intermediate subgoals closer to the current state. The grounded final goal enables backward subgoal planning to always remain aware of task completion, facilitating on-task prediction along the entire planning horizon. The subgoal-conditioned policy incorporates a learnable token to summarize the subgoal sequences and determines how each subgoal guides action extraction. Through extensive simulation and real-robot long-horizon experiments, we show that LBP outperforms existing fine-grained and forward planning methods, achieving SOTA performance. Project Page: https://lbp-authors.github.io
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。
