Humans leverage multiple sensor modalities when interacting with objects and discovering their intrinsic properties. Using the visual modality alone is insufficient for deriving intuition behind object properties (e.g., which of two boxes is heavier), making it essential to consider non-visual modalities as well, such as the tactile and auditory. Whereas robots may leverage various modalities to obtain object property understanding via learned exploratory interactions with objects (e.g., grasping, lifting, and shaking behaviors), challenges remain: the implicit knowledge acquired by one robot via object exploration cannot be directly leveraged by another robot with different morphology, because the sensor models, observed data distributions, and interaction capabilities are different across these different robot configurations. To avoid the costly process of learning interactive object perception tasks from scratch, we propose a multi-stage projection framework for each new robot for transferring implicit knowledge of object properties across heterogeneous robot morphologies. We evaluate our approach on the object-property recognition and object-identity recognition tasks, using a dataset containing two heterogeneous robots that perform 7,600 object interactions. Results indicate that knowledge can be transferred across robots, such that a newly-deployed robot can bootstrap its recognition models without exhaustively exploring all objects. We also propose a data augmentation technique and show that this technique improves the generalization of models. We release our code and datasets, here: https://github.com/gtatiya/Implicit-Knowledge-Transfer.

翻译：人类在与物体交互并发现其内在属性时会利用多种感觉模态。仅依赖视觉模态不足以推导物体属性的直觉（例如，两个盒子中哪个更重），因此需要考虑触觉和听觉等非视觉模态。虽然机器人可以通过学习性的物体交互操作（如抓取、举升和摇晃行为）利用多种模态来获得物体属性理解，但仍存在挑战：由于不同机器人配置下的传感器模型、观测数据分布和交互能力存在差异，一台机器人通过物体探索获得的隐含知识无法直接被另一台不同形态的机器人利用。为了避免从零开始学习交互式物体感知任务的昂贵过程，我们提出了一种多阶段投影框架，用于跨异构机器人形态迁移物体属性的隐含知识。我们在物体属性识别和物体身份识别任务上评估了该方法，使用了包含两台异构机器人执行7600次物体交互的数据集。结果表明，知识可以在机器人间迁移，使得新部署的机器人无需遍历所有物体即可快速启动其识别模型。我们还提出了一种数据增强技术，并表明该技术能提升模型的泛化能力。我们在以下地址开源了代码和数据集：https://github.com/gtatiya/Implicit-Knowledge-Transfer。