Cognitive Mapping and Planning for Visual Navigation (1702.03920v3)

Abstract: We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as 'going to a chair'. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation.

• The paper introduces CMP, a neural architecture that jointly integrates mapping and planning for effective visual navigation.
• It leverages spatial memory with a dynamically updated top-down belief map to adapt to partially observed surroundings.
• Evaluations in simulated and real-world scenarios show CMP outperforms traditional SLAM and LSTM-based approaches in navigation tasks.

Cognitive Mapping and Planning for Visual Navigation

The paper "Cognitive Mapping and Planning for Visual Navigation" by Gupta et al. introduces a neural architecture called the Cognitive Mapper and Planner (CMP), designed to navigate novel environments. This architecture is notable for its integration of mapping and planning into a joint system that learns from first-person visual inputs to navigate toward specified goals.

Architecture Overview

The CMP architecture is founded on two main principles: the incorporation of a unified framework for mapping and planning, and the use of spatial memory to handle incomplete observations. This results in the creation of a top-down belief map, which is dynamically updated as new visual information is acquired. This map is used alongside a differentiable neural net planner, which determines the optimal action at each time step.

Key Features

• Unified Framework: Unlike traditional SLAM-based approaches that separate mapping and planning, CMP holistically combines them, enabling more robust navigation under partially observed conditions.
• Spatial Memory: This component creates and updates an egocentric view of the world, allowing for more informed decision-making based on accumulated environmental data.
• Differentiable Planning: The planning mechanism is designed to be trainable and hierarchical, enabling efficient path planning even with limited sensory input.

Performance Evaluation

The authors demonstrate the capabilities of CMP in simulated environments derived from real-world building scans, comparing its performance against various baseline methodologies. CMP consistently outperforms both classical approaches and other learning-based architectures in these trials. Specifically, CMP showcases enhanced navigation abilities, following paths more effectively and handling semantically defined goals like "go to a chair."

Experimental Setup

The evaluation environments are sourced from the Stanford large-scale 3D Indoor Spaces (S3DIS) dataset, ensuring realistic visual navigation challenges. The experiments include tests on both geometric tasks and semantic tasks, where the former involves navigation to a designated point and the latter involves searching for specific objects such as chairs or doors.

Comparative Analysis

• Reactive Policies and LSTM Agents: These alternatives were tested to probe the necessity of a mapping component. While LSTM-based models provide some memory-related benefits, CMP distinguishes itself through superior structural awareness and adaptability.
• Classical Mapping and Planning: Purely geometric methods struggle, particularly when using RGB inputs. In contrast, CMP shows remarkable flexibility, capitalizing on its learned representations to anticipate and navigate unseen areas.

Real-World Deployment

Perhaps most compelling is the successful transfer of CMP to real-world robotic platforms. The authors describe deploying their trained navigation models onto a TurtleBot 2, achieving a 68% success rate across various complex navigation tasks, an impressive result given the simulations' reliance solely on virtual training data.

Future Directions and Implications

The paper outlines critical advancements in bridging cognitive and robotic methods for navigation. The CMP architecture not only pushes the boundaries of visual navigation research but also lays the groundwork for future exploration into areas such as dynamic environments and more complex semantic navigation tasks. Future prospects include addressing odometry imperfections and scaling the system for larger environments without metric representations.

By embedding mapping and planning within an end-to-end learnable framework, CMP embodies a promising shift towards versatile, adaptive navigation systems that could transform robotics and interactive AI applications. This work raises pertinent questions about the potential of AI systems to generalize from simulated experiences to real-world tasks, a frontier ripe for further investigation in the field of autonomous agents.