Rapid Exploration for Open-World Navigation with Latent Goal Models (2104.05859v5)

Abstract: We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration. Trained on a large offline dataset of prior experience, the model acquires a representation of visual goals that is robust to task-irrelevant distractors. We demonstrate our method on a mobile ground robot in open-world exploration scenarios. Given an image of a goal that is up to 80 meters away, our method leverages its representation to explore and discover the goal in under 20 minutes, even amidst previously-unseen obstacles and weather conditions. Please check out the project website for videos of our experiments and information about the real-world dataset used at https://sites.google.com/view/recon-robot.

• The paper introduces RECON, a novel system integrating latent variable models and topological memory for efficient goal-directed exploration in open environments.
• It employs an information bottleneck to generate compact visual representations that enhance generalization and reduce exploration time compared to baselines.
• Experimental results demonstrate RECON’s ability to discover goals up to 80 meters away in under 20 minutes, validating its real-world navigation effectiveness.

Analysis of "Rapid Exploration for Open-World Navigation with Latent Goal Models"

In the presented paper, the authors describe an advanced robotic learning system designed for autonomous exploration and navigation within diverse and open-world environments. The primary innovation of their approach lies in the integration of a latent variable model for distances and actions with a non-parametric topological memory of images. This system, named RECON, is demonstrated to navigate effectively in real-world scenarios, tackling challenges characteristic of open-world environments, such as variability in conditions and the presence of obstacles not seen during training.

Core Methodology

The authors propose a robotic system that utilizes a learned latent variable model to represent distances and actions. At the system's core, the information bottleneck method regularizes the learned policy. This approach yields several advantages:

1. Compact Visual Representation: It creates a concise visual representation of goals, enabling robust generalization to different environments.
2. Improved Generalization: The system effectively transfers knowledge acquired in one environment to new and unseen environments.
3. Sampling Feasible Exploration Goals: The mechanism allows the robot to sample feasible goals to explore efficiently.

Experimental Setup and Results

The system was trained on a large offline dataset comprised of prior experiences in various environments. The experiments were conducted using a mobile ground robot operating in multiple open-world environments. The training allowed RECON to build representations of visual goals that are highly resistant to task-irrelevant visual distractors.

Key results include:

• RECON successfully discovered and navigated to user-specified goals located as far as 80 meters away within a time frame of under 20 minutes.
• The system demonstrated robustness against previously unseen obstacles and different weather conditions, elucidating the adaptability of the latent goal models.

The evaluation compared the performance of RECON against several competitive baselines such as PPO with Random Network Distillation, InfoBot, and Active Neural SLAM (ANS), among others. RECON outperformed these methods in terms of exploration time and efficiency in goal-reaching after exploration. Specifically, backward steps by 50% in goal discovery and increased success rates in goal-reaching tasks underscore the effectiveness of RECON's design.

Implications and Future Directions

The implications of this research span both theoretical and practical domains:

• Theoretical Implications: The paper pushes forward the understanding of how latent variable models can be used for open-world navigation, providing insights into handling topological representations of environments. The approach could inspire more methods that capitalize on latent goal modeling for efficient exploration.
• Practical Implications: In practical robotic applications, customer-facing services such as autonomous delivery or exploration can benefit from these advancements by reducing time spent in navigating complex and changing environments.

For future work, the researchers aim to bolster theoretical guarantees concerning the exploration efficiency of such stochastic policies. They also indicate potential improvements by incorporating additional modalities for task specification, which could enhance the system's capability for long-range navigation tasks.

In conclusion, this paper presents a significant contribution to the field of robotic navigation in open-world environments. The development and application of RECON demonstrate the viability of latent goal models, fostered by information bottlenecks and topological memory, to achieve rapid and efficient exploration and goal discovery in real-world scenarios.