Abstract
Generalizing skill policies to novel conditions remains a key challenge in robot learning. Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it, leading to unpredictable policy failures. Alternatively, transfer learning approaches offer methods for trajectory generation robust to both changes in environment or tasks, but they remain data-hungry and lack accuracy in zero-shot generalization. We address these challenges by framing the problem in the context of task inversion learning and proposing a novel joint learning approach to achieve accurate and efficient knowledge transfer. Our method constructs a common representation of the forward and inverse tasks, and leverages auxiliary forward demonstrations from novel configurations to successfully execute the corresponding inverse tasks, without any direct supervision. We show the extrapolation capabilities of our framework via ablation studies and experiments in simulated and real-world environments that require complex manipulation skills with a diverse set of objects and tools, where we outperform diffusion-based alternatives.
Abstract (translated)
将技能策略泛化到新的条件中仍然是机器人学习中的一个关键挑战。模仿学习方法虽然数据效率高，但大多局限于训练区域，并且在输入数据超出此范围时会持续失败，导致政策失效的不可预测性。相比之下，迁移学习的方法为适应环境或任务的变化提供了生成轨迹的方式，但它们仍然需要大量数据并且在零样本泛化中缺乏准确性。 我们通过将问题置于任务逆向学习的框架下，并提出了一种新颖的联合学习方法来实现准确且高效的知识转移，以此应对这些挑战。我们的方法构建了正向和逆向任务之间的共同表示，并利用来自新配置的辅助正向演示，在没有直接监督的情况下成功执行相应的逆向任务。 我们通过消融研究和实验展示了框架在模拟和现实环境中所需的复杂操作技能上的外推能力，该实验涉及使用不同种类的对象和工具的情况。我们的方法在此类场景中超越了基于扩散的方法。
URL
https://arxiv.org/abs/2603.05576
