Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
This work addresses the need for generalizable assistive agents in robotics, though it appears incremental as it builds on existing successor representation methods.
The paper tackles the problem of visual navigation to novel goals with limited supervision by proposing HAUSR, which successfully reaches new targets and enables quick fine-tuning for new scenes.
Being able to navigate to a target with minimal supervision and prior knowledge is critical to creating human-like assistive agents. Prior work on map-based and map-less approaches have limited generalizability. In this paper, we present a novel approach, Hybrid Asynchronous Universal Successor Representations (HAUSR), which overcomes the problem of generalizability to new goals by adapting recent work on Universal Successor Representations with Asynchronous Actor-Critic Agents. We show that the agent was able to successfully reach novel goals and we were able to quickly fine-tune the network for adapting to new scenes. This opens up novel application scenarios where intelligent agents could learn from and adapt to a wide range of environments with minimal human input.