知识获取、适配与组合（KCAC）：一种面向机器人操作的跨任务课程学习框架
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation
AI总结：
针对强化学习在机器人操作中样本效率低、知识迁移不足的问题，提出 KCAC 跨任务课程学习框架，通过奖励重设计、子任务与课程参数优化，减少 40% 训练时间并提升 10% 成功率。
摘要：
强化学习（RL）在机器人操作中已展现出显著潜力，但面临样本效率低和可解释性不足的挑战，这限制了其在真实世界场景中的适用性。使智能体能够更深入地理解并更高效地适应多样化工作场景至关重要，而策略性的知识利用是这一过程中的关键因素。本文提出了一个知识获取、适配与组合（KCAC）框架，通过跨任务课程学习将知识迁移系统地整合到 RL 中。KCAC 在 CausalWorld 基准中的双块堆叠任务上接受评估，该基准是一个复杂的机器人操作环境。据我们所知，现有 RL 方法无法有效解决该任务，这反映出知识获取方面的缺陷。在本研究中，我们重新设计了基准奖励函数，移除了刚性约束和严格顺序，使智能体能够同时最大化总奖励，并实现灵活的任务完成。此外，我们定义了两个自行设计的子任务，并实施结构化的跨任务课程，以促进高效学习。结果表明，与传统 RL 方法相比，我们的 KCAC 方法将训练时间减少了 40%，同时将任务成功率提高了 10%。通过广泛评估，我们确定了优化学习效率的关键课程设计参数：子任务选择、转换时机和学习率，并为基于课程的 RL 框架提供了概念性指导。本研究为 RL 和机器人学习中的课程设计提供了有价值的见解。
英文摘要：
Reinforcement learning (RL) has demonstrated remarkable potential in robotic manipulation but faces challenges in sample inefficiency and lack of interpretability, limiting its applicability in real world scenarios. Enabling the agent to gain a deeper understanding and adapt more efficiently to diverse working scenarios is crucial, and strategic knowledge utilization is a key factor in this process. This paper proposes a Knowledge Capture, Adaptation, and Composition (KCAC) framework to systematically integrate knowledge transfer into RL through cross-task curriculum learning. KCAC is evaluated using a two block stacking task in the CausalWorld benchmark, a complex robotic manipulation environment. To our knowledge, existing RL approaches fail to solve this task effectively, reflecting deficiencies in knowledge capture. In this work, we redesign the benchmark reward function by removing rigid constraints and strict ordering, allowing the agent to maximize total rewards concurrently and enabling flexible task completion. Furthermore, we define two self-designed sub-tasks and implement a structured cross-task curriculum to facilitate efficient learning. As a result, our KCAC approach achieves a 40 percent reduction in training time while improving task success rates by 10 percent compared to traditional RL methods. Through extensive evaluation, we identify key curriculum design parameters subtask selection, transition timing, and learning rate that optimize learning efficiency and provide conceptual guidance for curriculum based RL frameworks. This work offers valuable insights into curriculum design in RL and robotic learning.
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。
