Object-to-Manipulation Graph for Affordance Navigation

Object navigation, whose goal is to let the agent to reach some places (or objects), has been a popular topic in embodied Artificial Intelligence (AI) researches. However, in our real-world applications, it is more practical to find the targets with particular goals, raising the new requirements of finding the places to achieve the particular functions. In this paper, we define a new task of affordance navigation, whose goal is to find possible places to accomplish the required functions, achieving some particular effects. We first introduce a new dataset for affordance navigation, collected by the proposed affordance algorithm. In order to avoid the high cost of labor, the groundtruth of each episode which is annotated with the interaction data provided by the AI2-THOR simulator. In addition, we also propose an affordance navigation framework, where an Object-to-Manipulation Graph (OMG) is constructed and optimized to emphasize the corresponding nodes (including object nodes and manipulation nodes). Finally, a navigation policy is implemented (trained by reinforcement learning) to guide the navigation to the target places. Experimental results on AI2-THOR simulator illustrate the effectiveness of the proposed approach, which achieves significant gains of 14.0% and 11.7% (on success rate and Success weighted by Path Length (SPL), respectively) over the baseline model.