7 research outputs found
CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning
Visual navigation tasks in real-world environments often require both
self-motion and place recognition feedback. While deep reinforcement learning
has shown success in solving these perception and decision-making problems in
an end-to-end manner, these algorithms require large amounts of experience to
learn navigation policies from high-dimensional data, which is generally
impractical for real robots due to sample complexity. In this paper, we address
these problems with two main contributions. We first leverage place recognition
and deep learning techniques combined with goal destination feedback to
generate compact, bimodal image representations that can then be used to
effectively learn control policies from a small amount of experience. Second,
we present an interactive framework, CityLearn, that enables for the first time
training and deployment of navigation algorithms across city-sized, realistic
environments with extreme visual appearance changes. CityLearn features more
than 10 benchmark datasets, often used in visual place recognition and
autonomous driving research, including over 100 recorded traversals across 60
cities around the world. We evaluate our approach on two CityLearn
environments, training our navigation policy on a single traversal. Results
show our method can be over 2 orders of magnitude faster than when using raw
images, and can also generalize across extreme visual changes including day to
night and summer to winter transitions.Comment: Preprint version of article accepted to ICRA 202
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Depth perception models are typically trained on non-interactive datasets
with predefined camera trajectories. However, this often introduces systematic
biases into the learning process correlated to specific camera paths chosen
during data acquisition. In this paper, we investigate the role of how data is
collected for learning depth completion, from a robot navigation perspective,
by leveraging 3D interactive environments. First, we evaluate four depth
completion models trained on data collected using conventional navigation
techniques. Our key insight is that existing exploration paradigms do not
necessarily provide task-specific data points to achieve competent unsupervised
depth completion learning. We then find that data collected with respect to
photometric reconstruction has a direct positive influence on model
performance. As a result, we develop an active, task-informed, depth
uncertainty-based motion planning approach for learning depth completion, which
we call DEpth Uncertainty-guided eXploration (DEUX). Training with data
collected by our approach improves depth completion by an average greater than
18% across four depth completion models compared to existing exploration
methods on the MP3D test set. We show that our approach further improves
zero-shot generalization, while offering new insights into integrating robot
learning-based depth estimation
Energy-Aware Ergodic Search: Continuous Exploration for Multi-Agent Systems with Battery Constraints
Continuous exploration without interruption is important in scenarios such as
search and rescue and precision agriculture, where consistent presence is
needed to detect events over large areas. Ergodic search already derives
continuous trajectories in these scenarios so that a robot spends more time in
areas with high information density. However, existing literature on ergodic
search does not consider the robot's energy constraints, limiting how long a
robot can explore. In fact, if the robots are battery-powered, it is physically
not possible to continuously explore on a single battery charge. Our paper
tackles this challenge, integrating ergodic search methods with energy-aware
coverage. We trade off battery usage and coverage quality, maintaining
uninterrupted exploration by at least one agent. Our approach derives an
abstract battery model for future state-of-charge estimation and extends
canonical ergodic search to ergodic search under battery constraints. Empirical
data from simulations and real-world experiments demonstrate the effectiveness
of our energy-aware ergodic search, which ensures continuous exploration and
guarantees spatial coverage.Comment: 7 pages, 7 figures, ICRA'2