从在线人类演示中学习主动机器人课程
Active Robot Curriculum Learning from Online Human Demonstrations
AI总结：
提出一种基于课程学习的主动 LfD 方法，通过优化在线人类演示的查询序列引导难度递增，显著提升了机器人的学习性能与样本效率，并改善了人类教学表现、适应性和可迁移性。
摘要：
从演示中学习（LfD）允许机器人从人类用户那里学习技能，但由于次优教学，尤其是未受训练的演示者的教学，其有效性可能会受到影响。主动 LfD 旨在通过让机器人主动请求演示来增强学习，从而改善这一情况。然而，这可能导致在各种任务情况之间频繁切换上下文，增加人类认知负荷并给演示引入错误。此外，主动 LfD 领域的少数先前研究探讨了这些主动查询策略如何影响用户体验之外的人类教学方面，这对于开发有益于机器人学习和人类教学的算法至关重要。为了应对这些挑战，我们提出了一种主动 LfD 方法，通过课程学习（CL）优化在线人类演示的查询序列，引导演示者在难度逐渐增加的情况下提供演示。我们在四个具有稀疏奖励的模拟机器人任务中评估了我们的方法，并进行了用户研究（N=26），以调查主动 LfD 方法对人类教学在教学表现、指导后教学适应性以及教学可迁移性方面的影响。我们的结果表明，就收敛策略的最终成功率和样本效率而言，与其他三种 LfD 基线相比，我们的方法显著提高了学习性能。此外，用户研究的结果表明，我们的方法显著减少了人类演示者所需的时间，并减少了失败的演示尝试。与另一种主动 LfD 基线相比，它还在已见和未见场景中增强了指导后的人类教学，这表明我们的方法实现了更强的教学表现、更高的指导后教学适应性以及更好的教学可迁移性。
英文摘要：
Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. However, this may lead to frequent context switches between various task situations, increasing the human cognitive load and introducing errors to demonstrations. Moreover, few prior studies in active LfD have examined how these active query strategies may impact human teaching in aspects beyond user experience, which can be crucial for developing algorithms that benefit both robot learning and human teaching. To tackle these challenges, we propose an active LfD method that optimizes the query sequence of online human demonstrations via Curriculum Learning (CL), where demonstrators are guided to provide demonstrations in situations of gradually increasing difficulty. We evaluate our method across four simulated robotic tasks with sparse rewards and conduct a user study (N=26) to investigate the influence of active LfD methods on human teaching regarding teaching performance, post-guidance teaching adaptivity, and teaching transferability. Our results show that our method significantly improves learning performance compared to three other LfD baselines in terms of the final success rate of the converged policy and sample efficiency. Additionally, results from our user study indicate that our method significantly reduces the time required from human demonstrators and decreases failed demonstration attempts. It also enhances post-guidance human teaching in both seen and unseen scenarios compared to another active LfD baseline, indicating enhanced teaching performance, greater post-guidance teaching adaptivity, and better teaching transferability achieved by our method.
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。
