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Tool use requires reasoning about the fit between an object's affordances andthe demands of a task. Visual affordance learning can benefit fromgoal-directed interaction experience, but current techniques rely on humanlabels or expert demonstrations to generate this data. In this paper, wedescribe a method that grounds affordances in physical interactions instead,thus removing the need for human labels or expert policies. We use an efficientsampling-based method to generate successful trajectories that provide contactdata, which are then used to reveal affordance representations. Our framework,GIFT, operates in two phases: first, we discover visual affordances fromgoal-directed interaction with a set of procedurally generated tools; second,we train a model to predict new instances of the discovered affordances onnovel tools in a self-supervised fashion. In our experiments, we show that GIFTcan leverage a sparse keypoint representation to predict grasp and interactionpoints to accommodate multiple tasks, such as hooking, reaching, and hammering.GIFT outperforms baselines on all tasks and matches a human oracle on two ofthree tasks using novel tools.

GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels