Affordance-based robot object retrieval

Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object's type such as "scissors" and/or visual attributes such as "red," thus limiting the robot to only known object classes. We develop a model to retrieve objects based on descriptions of their usage. The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects; and outputs the object that best satisfies the task specified by the verb. Our model directly predicts an object's appearance from the object's use specified by a verb phrase, without needing an object's class label. Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands. Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves a mean reciprocal rank of 77.4% on a held-out test set of unseen ImageNet object classes and 69.1% on unseen object classes and unknown nouns. Our model also achieves a mean reciprocal rank of 71.8% on unseen YCB object classes, which have a different image distribution from ImageNet. We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage (Video recordings of the robot demonstrations can be found at ). We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes (The dataset and code for the project can be found at https://github.com/Thaonguyen3095/affordance- language).