用混合扩散模型同时生成机器人动作和符号计划，提升长程任务成功率。
Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
- 结合离散符号计划与连续轨迹的混合扩散机制
- 在长程任务中成功率显著高于传统扩散模型
- 支持部分或完整符号条件下的灵活动作生成，适合复杂任务规划
构建能完成长程任务的机器人是人工智能领域的长期挑战。基于生成方法，特别是扩散模型，因其能够建模连续机器人轨迹而受到关注。然而我们发现，这类模型在涉及复杂决策的长程任务中表现不佳，且容易混淆不同行为模式，导致失败。为此，我们提出在生成连续轨迹的同时，同步生成高层符号计划。这需要一种新的离散变量扩散与连续扩散相结合的混合方法，显著优于基线模型。此外，该混合扩散过程实现了灵活的轨迹合成，可对部分或完整的符号条件进行条件化，从而支持更鲁棒的任务规划。
原文摘要 · Abstract (English)
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy this, we propose to augment continuous trajectory generation by simultaneously generating a high-level symbolic plan. We show that this requires a novel mix of discrete variable diffusion and continuous diffusion, which dramatically outperforms the baselines. In addition, we illustrate how this hybrid diffusion process enables flexible trajectory synthesis, allowing us to condition synthesized actions on partial and complete symbolic conditions.
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