Identification of Prototypical Task Executions Based on Smoothness as Basis of Human-to-Robot Kinematic Skill Transfer

平滑度衡量技能而非成功：在回合长度控制下对已发表的轨迹平滑度筛选指标的审计

Smoothness Ranks Skill, Not Success: An Audit of Published Trajectory-Smoothness Curation Metrics Under Episode-Length Controls

该研究审计了 SAL、TED 等轨迹平滑度筛选指标，发现其在回合长度控制下有效性不足，TED 筛选的下游成功显著低于随机选择，且指标效果在不同策略配置间无法直接迁移。

RPG: 为人类oid战斗中平滑多技能过渡的鲁棒策略门控

RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting

本文提出RPG框架，通过动作过渡随机化和时间随机化训练统一策略，实现人类oid多技能平滑稳定过渡，并设计控制管道整合步行/跑步与战斗技能，实验证明其有效性与鲁棒性。

Generalized Task-Parameterized Skill Learning

Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been recently developed. This model has achieved reliable performance in areas such as human-robot collaboration and dual-arm manipulation. However, the crucial task frames and associated parameters in this learning framework are often set by the human teacher, which renders three problems that…

Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills

Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration. Learning from demonstration (LfD) provides a promising way to learn these kind of complex manipulation skills even from non-technical users. However, it is challenging for existing LfD methods to efficiently learn skills that can generalize to task specifications that are not covered by demonstrations. In this paper, we introduce a state transition …

基于弹性地图的机器人演示学习

Robot Learning from Demonstration Using Elastic Maps

本文提出一种基于优化的演示学习方法，将演示编码为弹性地图，通过三个具有物理意义的凸优化目标拟合技能模型，并在UR5e机械臂上验证了其优于其他LfD方法的性能与灵活性。

Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

Learning from Demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This paper presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinfor…