Learning Exploration Policies for Navigation (1903.01959v1)

Abstract: Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of task-rewards. We propose a learning-based approach and investigate different policy architectures, reward functions, and training paradigms. We find that the use of policies with spatial memory that are bootstrapped with imitation learning and finally finetuned with coverage rewards derived purely from on-board sensors can be effective at exploring novel environments. We show that our learned exploration policies can explore better than classical approaches based on geometry alone and generic learning-based exploration techniques. Finally, we also show how such task-agnostic exploration can be used for down-stream tasks. Code and Videos are available at: https://sites.google.com/view/exploration-for-nav.

• The paper presents a novel approach that combines imitation and reinforcement learning with an intrinsic, coverage-based reward to develop autonomous exploration policies.
• It details the integration of spatial memory and recurrent neural networks processing RGB-D images and occupancy maps for robust long-term navigation.
• Experimental results in the House3D environment show improved performance over classical frontier-based and curiosity-driven methods, enhancing downstream task effectiveness.

Learning Exploration Policies for Navigation

The paper "Learning Exploration Policies for Navigation" addresses the critical yet underexplored facet of equipping agents with efficient exploration capabilities in novel 3D environments, divorced from task-specific rewards. Prior approaches to navigation either relied heavily on geometrical reconstruction and path planning or designed learning-based policies focused on particular tasks or pre-explored environments. This paper articulates a novel learning-based methodology that aims to enable autonomous exploration, potentially enhancing downstream task performance in unseen environments without prior knowledge or human intervention for map building.

Approach and Methodology

The authors propose a comprehensive approach encompassing architectural design, reward function development, and training paradigms to cultivate task-agnostic exploration policies. They leverage policies with spatial memory, initially bootstrapped through imitation learning from human exploration data. Subsequently, these policies are fine-tuned with an intrinsic reward mechanism based on the coverage achieved, utilizing on-board sensory inputs. In terms of architecture, the policy leverages RGB-D image data and occupancy maps, processed through recurrent networks, to maintain coherent long-term behavior beneficial for exploration.

1. Policy Architecture:
   • The architecture integrates RGB images and 3D occupancy maps to foster semantic cue recognition and obstacle avoidance.
   • It employs a recurrent neural network (RNN) to handle long horizon temporal dependencies crucial for navigating and exploring complex environments.
2. Reward Mechanism:
   • The intrinsic reward is designed with a focus on coverage, allowing the agent to optimize its policy by maximizing the known traversable space.
   • A collision penalty is integrated to discourage inefficient movements, ensuring the agent learns to avoid obstacles effectively.
3. Training Paradigms:
   • The training starts with imitation learning using human-generated exploration trajectories, providing a foundational understanding of semantic aspects like doors.
   • The policy is further refined using reinforcement learning (RL) with Proximal Policy Optimization (PPO), capitalizing on the intrinsic reward to improve exploration outcomes.

Experimental Validation

The experimental evaluation is conducted within the House3D environment, emphasizing generalization to ensure policies don't merely memorize but truly learn to explore new environments. Key findings include:

• Impact of Estimation Noise: The proposed learning-based approach outperformed classical frontier-based methods, particularly under scenarios involving noise in state estimation, thus demonstrating robustness gained through learning.
• Comparison with Curiosity-Based Methods: The exploration policy surpassed the baseline of curiosity-driven exploration by a significant margin, indicating the effectiveness of the coverage-centric reward function.
• Downstream Task Enhancement: Utilization of the learned exploration policies in downstream navigation tasks exhibited marked improvement in task performance metrics, such as SPL (Success weighted by Path Length), compared to baseline methods without exploration experience.

Implications and Future Work

This research underscores the importance of robust exploration capabilities as foundational to enhancing navigation and task performance in AI agents. By addressing the exploration phase independently of specific tasks, the authors open avenues for deploying AI in real-world environments where pre-definition and exhaustive mapping aren't feasible.

Future developments could explore incorporating more sophisticated semantic understanding and integrating richer sensory inputs. Additionally, investigating the generalization of the proposed architectures to include dynamic and interactive environments could offer substantial progress toward real-world applicability.

In conclusion, this paper contributes a structured methodology for learning exploration policies that advance the efficacy and adaptability of navigation systems, thereby broadening the scope of autonomous agent capabilities in uncharted and realistic environments.