让机器人像人一样无意识模仿动作，速度翻倍还更稳定。
Subconscious Robotic Imitation Learning
- 用记忆碎片+认知卸载，减少模型推理延迟
- 双臂任务执行速度提升100%~200%，成功率更高
- 适合需要快速响应的复杂操控场景
尽管机器人模仿学习（RIL）对具身智能机器人前景广阔，但现有方法依赖计算量大的多模型轨迹预测，导致执行缓慢、实时性差。受人类潜意识持续处理经验与感知信息的启发，我们提出潜意识机器人模仿学习（SRIL），将认知卸载与历史动作片段结合，降低模型推断带来的延迟，从而加速任务执行。该过程进一步通过潜意识降采样和模式增强学习策略，利用量化采样技术提取高语义信息，提升操作效率。实验表明，在综合双臂任务中，SRIL的执行速度比当前最优策略快100%至200%，且成功率持续更高。
原文摘要 · Abstract (English)
Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions, resulting in slow execution and limited real-time responsiveness. Instead, human beings subconscious can constantly process and store vast amounts of information from their experiences, perceptions, and learning, allowing them to fulfill complex actions such as riding a bike, without consciously thinking about each. Inspired by this phenomenon in action neurology, we introduced subconscious robotic imitation learning (SRIL), wherein cognitive offloading was combined with historical action chunkings to reduce delays caused by model inferences, thereby accelerating task execution. This process was further enhanced by subconscious downsampling and pattern augmented learning policy wherein intent-rich information was addressed with quantized sampling techniques to improve manipulation efficiency. Experimental results demonstrated that execution speeds of the SRIL were 100\% to 200\% faster over SOTA policies for comprehensive dual-arm tasks, with consistently higher success rates.
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