Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention
This paper presents an innovative paper tackling the challenge of integrating vision-based navigation with language-based assistance in indoor environments. The proposed framework, Vision-based Navigation with Language-based Assistance (VNLA), addresses the practical problem where a visually-enabled mobile agent, such as a robot, needs to fulfill object-finding tasks within complex, photorealistic environments. Instead of relying on detailed step-by-step instructions, tasks are specified with high-level end-goals like "find a pillow in one of the bedrooms," reflecting real-world scenarios where a requester may not know specific navigation paths.
Contributions
The primary contributions of this paper are multifold:
- Task Formulation: Vision-based Navigation with Language-based Assistance (VNLA): The paper introduces a task where the agent seeks to find objects in indoor environments with aid through language. This task is structurally sound as it incorporates scenarios where the agent must recognize when it is lost and request assistance in the form of language subgoals.
- Novel Learning Framework: Imitation Learning with Indirect Intervention (I3L): The I3L framework extends imitation learning by maintaining the presence of an advisor during both training and testing, providing indirect interventions via language subgoals. The advisor influences agent decisions by modifying the environment, which necessitates the learning of interpretation mechanisms to effectively execute the interventions.
- Empirical Results: This framework is validated using the Matterport3D simulator, a large-scale simulator of real indoor environments. The empirical results demonstrate significant improvements in success rates for tasks in both seen and unseen environments when compared to baseline models.
Methodology and Implementation Details
The VNLA task is implemented using a navigation agent that experiences the environment through a monocular visual input. The agent relies on an action space consisting of basic navigation operations such as turning and moving forward. When the agent encounters situations where it cannot progress, it requests subgoals from an advisor that guide it with simpler navigation instructions.
The learning approach leverages the I3L framework. Unlike standard imitation learning models, the agent operates in a dynamic environment, augmented through the advisor's language-based interventions. The behaviors of the agent are determined using a combination of conventional imitation learning techniques and behavior cloning, particularly under guidance involving subgoals. Such a combination allows the agent to adhere to trajectories suggested by its advisor and effectively reduce intervention interpretation error.
Results and Implications
The deployment of VNLA in the Matterport3D environment demonstrated robust results: agents using the proposed framework consistently showed improved task success in finding objects across varied and previously unseen environments. Metrics such as success rate and mean navigation error were used to corroborate these findings.
Moreover, these results imply that this model holds promise for future applications in autonomous systems where real-time decision-making is augmented by language-based assistance. The framework could potentially be adapted for broader applications beyond navigation, such as robotics and AI-driven tools that interact with humans in natural language.
Future Directions
The research suggests further longer-term developments, including the exploration of more natural, fully linguistic question-and-answer interactions between the agent and its advisor. Additionally, extending such models to real-world scenarios and bridging the gap between simulation environments and physical deployment in mobile robots remain attractive avenues for future work. More complex tasks involving real-time decision-making and dynamic interaction with human operators can also benefit from these advancements in combining visual and linguistic processing within a unified framework.