用于从演示中学习运动基元的条件神经专家过程
Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration
AI总结：
提出条件神经专家过程 CNEP，无需模态标签即可将多模态演示分配给专家网络，在多模态数据集和真实机器人任务上优于多种 LfD 基线，并支持在线适应与可解释的专家选择。
摘要：
从演示中学习（Learning from Demonstration，LfD）是机器人技能获取中广泛使用的技术。然而，同一技能的演示可能表现出显著差异，或者学习系统可能试图同时获取同一技能的不同实现方式，这使得将这些运动编码为运动基元具有挑战性。为应对这些挑战，我们提出了一个 LfD 框架，即条件神经专家过程（Conditional Neural Expert Processes，CNEP），该框架学习利用潜在空间中的内在信息，将来自不同模态的演示分配给不同的专家网络，以使专家与编码表示相匹配。CNEP 不需要关于轨迹属于哪种模态的监督。我们将 CNEP 的性能与高斯混合模型、概率运动基元和稳定运动基元等广泛使用且功能强大的 LfD 方法进行了比较，结果表明我们的方法在多模态轨迹数据集上优于这些基线方法。结果揭示了运动基元建模性能的增强，从而生成更准确反映专家所示轨迹的轨迹，尤其是在技能演示包含来自各种轨迹的交点时。我们在两个真实机器人任务上评估了 CNEP 模型，即避障和拾取放置任务，这些任务要求机器人学习多模态运动轨迹，并在给定目标环境条件下执行正确的基元。我们还展示了我们的系统能够通过在线条件机制即时适应环境变化。最后，我们认为 CNEP 通过自主发现离散行为基元并提供有关其专家选择决策的概率值，提供了更好的可解释性和可理解性。
英文摘要：
Learning from Demonstration (LfD) is a widely used technique for skill acquisition in robotics. However, demonstrations of the same skill may exhibit significant variances, or learning systems may attempt to acquire different means of the same skill simultaneously, making it challenging to encode these motions into movement primitives. To address these challenges, we propose an LfD framework, namely the Conditional Neural Expert Processes (CNEP), that learns to assign demonstrations from different modes to distinct expert networks utilizing the inherent information within the latent space to match experts with the encoded representations. CNEP does not require supervision on which mode the trajectories belong to. We compare the performance of CNEP against widely used and powerful LfD methods such as Gaussian Mixture Models, Probabilistic Movement Primitives, and Stable Movement Primitives and show that our method outperforms these baselines on multimodal trajectory datasets. The results reveal enhanced modeling performance for movement primitives, leading to the synthesis of trajectories that more accurately reflect those demonstrated by experts, particularly when the skill demonstrations include intersection points from various trajectories. We evaluated the CNEP model on two real-robot tasks, namely obstacle avoidance and pick-and-place tasks, that require the robot to learn multi-modal motion trajectories and execute the correct primitives given target environment conditions. We also showed that our system is capable of on-the-fly adaptation to environmental changes via an online conditioning mechanism. Lastly, we believe that CNEP offers improved explainability and interpretability by autonomously finding discrete behavior primitives and providing probability values about its expert selection decisions.
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。
