Abstract
Furniture assembly remains an unsolved problem in robotic manipulation due to its long task horizon and nongeneralizable operations plan. This paper presents the Tactile Ensemble Skill Transfer (TEST) framework, a pioneering offline reinforcement learning (RL) approach that incorporates tactile feedback in the control loop. TEST's core design is to learn a skill transition model for high-level planning, along with a set of adaptive intra-skill goal-reaching policies. Such design aims to solve the robotic furniture assembly problem in a more generalizable way, facilitating seamless chaining of skills for this long-horizon task. We first sample demonstration from a set of heuristic policies and trajectories consisting of a set of randomized sub-skill segments, enabling the acquisition of rich robot trajectories that capture skill stages, robot states, visual indicators, and crucially, tactile signals. Leveraging these trajectories, our offline RL method discerns skill termination conditions and coordinates skill transitions. Our evaluations highlight the proficiency of TEST on the in-distribution furniture assemblies, its adaptability to unseen furniture configurations, and its robustness against visual disturbances. Ablation studies further accentuate the pivotal role of two algorithmic components: the skill transition model and tactile ensemble policies. Results indicate that TEST can achieve a success rate of 90\% and is over 4 times more efficient than the heuristic policy in both in-distribution and generalization settings, suggesting a scalable skill transfer approach for contact-rich manipulation.
Abstract (translated)
家具组装仍然是一个在机器人操作中未解决的难题，由于其长远的任务范围和无法扩展的操作计划。本文介绍了一种首创的在线强化学习（RL）方法：Tactile Ensemble Skill Transfer（TEST）框架，该框架在控制环中包含了触觉反馈。TEST的核心设计是学习高级规划技能转移模型以及一系列自适应的技能内化目标达成策略。这样的设计旨在以更通用的方式解决机器人家具组装问题，从而使技能的传承更加顺畅。 首先，我们从一系列启发式策略和轨迹中采样演示，包括一系列随机的子技能段，从而使机器人获得丰富的轨迹，捕捉技能阶段、机器人状态、视觉指示和关键的是，触觉信号。利用这些轨迹，我们的离线RL方法可以辨别技能终止条件并协调技能转换。我们的评估显示，TEST在离散家具组装方面表现出卓越的性能，其对未见过的家具配置的适应性，以及对抗视觉干扰的鲁棒性。消融研究进一步强调了两个算法组件的重要性：技能转移模型和触觉集成策略。结果表明，TEST可以在离散和泛化设置中获得90%的成功率，而在分布和通用设置中，其效率是启发式策略的4倍以上，表明了为接触密集操作实现可扩展技能转移的方法。
URL
https://arxiv.org/abs/2404.17684
