https://umd.zoom.us/j/8188451867?pwd=RXdUVHd2eFdFOFluVElFVjVYbmRlZz09&omn=92316383987

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 in 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 not only direct observations but also predictions of what lies ahead to better navigate through 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, which is a crucial requirement for energy-constrained aerial robots.

Deploying a team of robots can also accelerate mapping. Our algorithms benefit from this setup as more observation results in more accurate predictions and further improves the task efficiency in the aforementioned tasks.

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 while achieving comparable coverage performance as classical approaches. We find that differentiable design is instrumental here for end-to-end task-oriented learning. Building on this, we present a differentiable decision-making framework that consists of a differentiable decentralized planner and a differentiable perception module for dynamic tracking.

In the third part of the dissertation, we show how to harness semantic patterns in the environment. Adding semantic context to the observations can help the robots decipher the relations between objects and infer what may happen next based on the activity around them. We present a pipeline using vision-language models to capture a wider scene using an overhead camera to provide assistance to humans and robots in the scene. We use this setup to implement an assistive robot to help humans with daily tasks, and then present a semantic communication-based collaborative setup of overhead-ground agents, highlighting the embodiment-specific challenges they may encounter and how they can be overcome.

The first three parts employ learning-based methods for predicting the environment. However, if the predictions are incorrect, this could pose a risk to the robot and its surroundings. The third part of the dissertation presents risk management methods with meta-reasoning over the predictions. We study two such methods: one extracting uncertainty from the prediction model for risk-aware planning, and another using a heuristic to adaptively switch between classical and prediction-based planning, resulting in safe and efficient robot navigation.