Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning (1909.01871v6)

Abstract: Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop "Help, Anna!" (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments. We publicly release code and data at https://github.com/khanhptnk/hanna . A video demo is available at https://youtu.be/18P94aaaLKg .

Overview of "Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning"

This paper presents an innovative approach to solving object-finding tasks in indoor environments using a framework known as "Help, Anna!" (HANNA). This framework simulates the interaction between mobile agents and human-like assistants who provide multimodal guidance through natural language and visual instructions. The goal is to enhance an agent's ability to navigate through unexplored environments by leveraging automated assistance, while minimizing reliance on human interventions.

Contributions

The core contributions of this paper are twofold: the introduction of the HANNA simulator and the development of novel imitation learning techniques specifically designed for this framework. These contributions are detailed as follows:

1. HANNA Simulator: The simulator offers a photo-realistic environment where an agent can fulfill object-finding tasks by soliciting guidance from Automatic Natural Navigation Assistants (ANNAs). These assistants are designed to simulate human-like help, providing instructions when the agent requests it. The simulator is built upon the Matterport3D environment, integrating previously collected natural language instructions to create a realistic setting for training agents.
2. Hierarchical Memory-Augmented Neural Model: The proposed method introduces a hierarchical model equipped with a memory-augmented architecture. This model is capable of handling multiple levels of decision-making, allowing the agent to manage both navigation and help-request strategies effectively. The hierarchical approach enables efficient processing of natural language instructions and enhances the agent's decision-making capabilities.
3. Retrospective Curiosity-Encouraging Imitation Learning: The paper proposes a novel imitation learning algorithm that combines curiosity-driven exploration with retrospective analysis. The curiosity mechanism discourages the repetition of past mistakes, while retrospective reasoning aids in determining optimal decision points for requesting assistance. This training strategy ensures the agent learns efficiently from both successful and unsuccessful navigation attempts.

Experimental Evaluation

Empirical results presented in the paper illustrate the effectiveness of the proposed methods. The agent trained using this framework achieved a significant improvement in task success rates compared to baseline methods, both in environments seen during training and in novel settings. Specifically, the paper reports a success rate of 47% in previously unseen environments, indicating robust generalization capabilities.

The agent's ability to learn when to request help, as well as how to interpret it, significantly boosted its performance, particularly in unexplored environments where navigating without assistance was substantially more challenging. The use of natural language for assistance proved beneficial, as demonstrated by the higher success rates when language instructions were incorporated alongside visual cues.

Implications and Future Directions

The work has notable implications for the development of intelligent agents capable of operating in dynamic and partially known environments. By integrating natural language processing with visual navigation, the framework paves the way for more sophisticated human-agent interactions in real-world applications, such as robotic assistants and autonomous navigation systems.

Furthermore, the retrospective and curiosity-driven learning framework offers a new perspective on exploration-exploitation trade-offs in reinforcement learning. This approach could be extended to other domains where agents must adapt quickly to unfamiliar situations with limited external guidance.

Future research may focus on enhancing the realism of agent-human interactions within the HANNA framework. This could involve developing agents that engage in more nuanced dialogues, formulate questions, or execute more complex tasks based on higher-level instructions. Additionally, a theoretical foundation for modeling human-assistance scenarios could provide insights to optimize help-request strategies further.

In conclusion, "Help, Anna!" demonstrates the potential of combining imitation learning, memory-augmented models, and multimodal communication to improve navigation tasks. It sets a precedent for future advancements in the field of intelligent navigation and human-robot collaboration.