Enhanced Robot Planning and Perception through Environment Prediction (2410.08560v1)

Abstract: Mobile robots rely on maps to navigate through an environment. In the absence of any map, the robots must build the map online from partial observations as they move in the environment. Traditional methods build a map using only direct observations. In contrast, humans identify patterns in the observed environment and make informed guesses about what to expect ahead. Modeling these patterns explicitly is difficult due to the complexity of the environments. However, these complex models can be approximated well using learning-based methods in conjunction with large training data. By extracting patterns, robots can use direct observations and predictions of what lies ahead to better navigate an unknown environment. In this dissertation, we present several learning-based methods to equip mobile robots with prediction capabilities for efficient and safer operation. In the first part of the dissertation, we learn to predict using geometrical and structural patterns in the environment. Partially observed maps provide invaluable cues for accurately predicting the unobserved areas. We first demonstrate the capability of general learning-based approaches to model these patterns for a variety of overhead map modalities. Then we employ task-specific learning for faster navigation in indoor environments by predicting 2D occupancy in the nearby regions. This idea is further extended to 3D point cloud representation for object reconstruction. Predicting the shape of the full object from only partial views, our approach paves the way for efficient next-best-view planning. In the second part of the dissertation, we learn to predict using spatiotemporal patterns in the environment. We focus on dynamic tasks such as target tracking and coverage where we seek decentralized coordination between robots. We first show how graph neural networks can be used for more scalable and faster inference.

• The paper proposes a framework that integrates structural, spatiotemporal, and semantic prediction techniques to improve mobile robot planning and perception.
• By using methods like ProxMaP and graph neural networks for decentralized planning, the study demonstrates significant gains in obstacle prediction and runtime efficiency.
• The paper addresses risk management with hybrid planning and Bayesian uncertainty measures, ensuring robust and safe operations in complex, dynamic settings.

Enhanced Robot Planning and Perception Through Environment Prediction

In the domain of robotics, mobile robot navigation in unknown environments is a critical capability. Traditional navigation methods often rely on building maps through direct observations, a process that can be limiting in dynamic and complex environments. This paper addresses this challenge by proposing a comprehensive framework that leverages environmental predictions to enhance robot planning and perception. By integrating learning-based methods to extract structural, spatiotemporal, and semantic patterns from an environment, the research demonstrates improved navigation efficiency and safety.

Structural and Geometrical Pattern Prediction

The paper begins by exploring the application of structural and geometrical patterns to improve 2D and 3D perception for robots. In particular, the authors introduce ProxMaP, a self-supervised method for proximal occupancy map prediction in indoor settings. It strategically predicts immediate surroundings, leading to efficient navigation by focusing on obstacle shapes rather than memorizing room layouts. Furthermore, the PredNBV algorithm exploits 3D shape completion to enhance the next-best-view planning for UAVs, facilitating better object reconstruction from partial observations. These innovations reflect a significant improvement in point cloud prediction and model-free planning over traditional approaches.

Spatiotemporal Patterns and Predictive Planning

The study extends the concept of prediction to dynamic environments through the use of spatiotemporal patterns. Graph neural networks are leveraged to understand and predict interactions among decentralized multi-robot teams. The proposed decentralized coverage planner, D2CoPlan, showcases the potential of learning-based approaches to optimize target tracking in complex tasks. A key aspect is the framework's scalability, which enables effective operation in large-scale scenarios, significantly outperforming classical algorithms in runtime efficiency.

Semantic Pattern Utilization

Addressing the importance of semantic context, the paper highlights the use of LLMs and vision-LLMs (VLMs) to foster human-robot collaboration. By predicting future human needs, the assistive framework aims to enhance robot capabilities in assisting daily human tasks. The integration of semantic understanding through these models ensures that robots can more effectively interpret and interact within human-centric environments.

Risk Management and Safety

One of the critical concerns with predictive models in robotics is the inherent risk associated with erroneous predictions. The paper presents methods for managing this risk by introducing hybrid planners that can seamlessly switch between classical and learning-based approaches, guided by explicit heuristic evaluations. Additionally, the utilization of Bayesian networks for uncertainty extraction enables safer planning decisions, ensuring robust operation in unpredictable settings.

Implications and Future Directions

The proposed framework successfully addresses the challenge of limited perception in robotics by introducing sophisticated predictive mechanisms tailored to diverse operational scenarios. The theoretical contributions set a precedent for future developments in AI-powered robotics, emphasizing the potential to bridge the gap between current limitations and the vision of fully autonomous, intelligent agents.

For future research, enhancing the robustness of prediction models to handle a wider variety of environmental perturbations remains a crucial avenue. Moreover, continuous improvement in semantic understanding and risk management strategies will be instrumental in realizing the next generation of collaborative and adaptive robotic systems. The ongoing exploration of training methodologies, data requirements, and computational efficiencies will further deepen the impact of learning-based approaches in robotics.

In conclusion, the paper lays a foundational framework for improving mobile robot navigation through innovative predictive methodologies, offering extensive implications for research and application in AI-driven robotic systems.