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English(EN) Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

机器人研究人员通过迁移学习增强运动规划

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新框架 iCEM+TL，以提高机器人操作任务的低级运动规划效率。该方法结合了样本高效交叉熵方法 (iCEM) 和迁移学习 (TL)，将参数从简单任务转移到更复杂的任务。该框架还通过任务分解来整合奖励重塑 (RR)，以实现堆叠和货架放置等特定动作。模拟显示成功率提高了 23%，并且该方法已在真实的 Franka Emika 机器人上成功演示。 AI

影响 通过提高复杂任务的规划效率和成功率，增强了机器人操作能力。

排序理由 该集群包含一篇详细介绍机器人运动规划新方法的学术论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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机器人研究人员通过迁移学习增强运动规划

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yuanzhi He, Victor Romero-Cano, Jos\'e J. Pati\~no, Juan David Hern\'andez, William Sawtell, Gualtiero Colombo ·

    通过零样本迁移学习实现机器人操作任务的样本高效低级运动规划

    arXiv:2606.06041v1 Announce Type: cross Abstract: As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Met…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Gualtiero Colombo ·

    通过零样本迁移学习实现机器人操作任务的样本高效低级运动规划

    As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising po…

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