Combining Optimal Control and Learning for Visual Navigation in Novel Environments (1903.02531v2)

Abstract: Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through on-board sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel environments as compared to purely geometric mapping-based or end-to-end learning-based alternatives. Our approach does not rely on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world. Videos describing our approach and experiments are available on the project website.

Technical Evaluation of Combining Optimal Control and Learning for Visual Navigation in Novel Environments

The technical paper entitled "Combining Optimal Control and Learning for Visual Navigation in Novel Environments" by Somil Bansal et al., presents a framework for autonomous navigation that efficaciously integrates model-based control with learning-based perception components. The paper focuses on robotic navigation in environments that are unknown a priori and employs RGB images from an onboard camera to pilot the robot toward target locations.

Framework Overview

The research introduces LB-WayPtNav, an approach comprising two primary modules: a perception module and a planning and control module. The perception module uses Convolutional Neural Networks (CNNs) to derive navigation waypoints from current RGB observations, incorporating both the current vehicle speed and the desired goal location in the system's coordinate frame. The planning module, grounded in model-based control, generates dynamically feasible spline trajectories from these predicted waypoints.

Methodological Insights

The key innovation of this framework lies in its robust amalgamation of learning-based waypoint prediction and classical optimal control. Unlike purely geometric map-based approaches, this hybrid method eschews building complete 3D maps and instead relies on partial views to predict high-level navigational decisions. The learning component leverages statistical regularities to hypothesize about unseen sections of an environment, while the optimal control ensures that the produced trajectory is smooth and dynamically stable, adhering to the robot's physical constraints.

Empirical Results

The LB-WayPtNav is evaluated against classical geometric mapping approaches and End-to-End (E2E) learning methodologies across both simulation tests and real-world scenarios. In simulations, this framework achieves a success rate of 80.65%, demonstrating significant improvements in trajectory smoothness relative to E2E methodologies. In deployment with hardware experiments, LB-WayPtNav shows adaptability to novel, unstructured environments and exhibits robustness across a variety of lighting conditions, with a 95% success rate, outperforming geometry-based navigation models which face challenges due to imperfect depth imaging.

Theoretical and Practical Implications

The implications of this approach extend both theoretically and practically. Theoretically, the work contributes to an understanding of how learning systems can synthesize environmental observations with robust controls to improve navigation efficacy and adaptability. From a practical perspective, LB-WayPtNav's ability to operate without relying on detailed depth maps suggests potential applications in resource-constrained systems where sensor capabilities are limited. Moreover, the successful direct transfer from simulation to real-world deployment underscores the reliability and potential cost savings in training autonomous systems across varied environments.

Future Prospects

Looking forward, the paper suggests that augmenting the system's perceptual capabilities might further enhance navigation accuracy, especially in sophisticated or dynamic scenes. Exploring mechanisms for incorporating spatial memory could improve performance for longer range tasks or situations requiring backtracking capabilities. Additionally, further investigation into scaling the framework across different types of vehicles could be of practical relevance to a wider array of autonomous applications.

In conclusion, the integration of optimal control with perception learning presents a promising avenue for advancing autonomous navigation capabilities in domains where environmental predictability is limited or resource constraints are a critical factor. The insights yielded by this research could catalyze further innovations in the fields of robotics and autonomous systems.