Abstract-- Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration.

This work examines the question: How can a robot explore efficiently? We conduct a human user study to gain insights into effective exploration strategies. These insights, in turn, inform the design of our reinforcement learning-based exploration policy, leveraging global map predictions and other environmental contexts and enabling state-of-the-art performance. Abstract -- Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-constrained paths that maximize information gain. Despite advances in autonomous exploration, existing algorithms still fall short of human performance, particularly in structured environments where predictive cues exist but are underutilized. Guided by insights from our user study, we introduce MapExRL, which improves robot exploration efficiency in structured indoor environments by enabling longer-horizon planning through reinforcement learning (RL) and global map predictions. Our framework generates global map predictions from the observed map, which our policy utilizes, along with the prediction uncertainty, estimated sensor coverage, frontier distance, and remaining distance budget, to assess the strategic long-term value of frontiers. By leveraging multiple frontier scoring methods and additional context, our policy makes more informed decisions at each stage of the exploration. We evaluate our framework on a real-world indoor map dataset, achieving up to an 18.8% improvement over the strongest state-of-the-art baseline, with even greater gains compared to conventional frontier-based algorithms. This work involved human subjects or animals in its research.

Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. Our code is available at https://theairlab.org/SALON.

Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are often predictable and composed of repeating patterns. Most existing approaches, such as conventional frontier approaches, have difficulty leveraging the predictability and explore with simple heuristics such as `closest first'. Recent works use deep learning techniques to predict unknown regions of the map, using these predictions for information gain calculation. However, these approaches are often sensitive to the predicted map quality or do not reason over sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12.4% improvement than representative map-prediction based exploration and 25.4% improvement than nearest frontier approach.

The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy. To overcome this limitation, we propose a general framework based on fusing the future information (i.e. future fusion) for self-supervision. Recent approaches exploit this future information alongside the hand-crafted heuristics to directly supervise the targeted downstream tasks (e.g. traversability estimation). However, in this paper, we opt for a more general line of development - time-efficient completion of the highest resolution (i.e. 2cm per pixel) BEV map in a self-supervised manner via future fusion, which can be used for any downstream tasks for better longer range prediction. To this end, first, we create a high-resolution future-fusion dataset containing pairs of (RGB / height) raw sparse and noisy inputs and map-based dense labels. Next, to accommodate the noise and sparsity of the sensory information, especially in the distal regions, we design an efficient realization of the Bayes filter onto the vanilla convolutional network via the recurrent mechanism. Equipped with the ideas from SOTA generative models, our Bayesian structure effectively predicts high-quality BEV maps in the distal regions. Extensive evaluation on both the quality of completion and downstream task on our future-fusion dataset demonstrates the potential of our approach.

Autonomous off-road driving requires understanding traversability, which refers to the suitability of a given terrain to drive over. When offroad vehicles travel at high speed ($>10m/s$), they need to reason at long-range ($50m$-$100m$) for safe and deliberate navigation. Moreover, vehicles often operate in new environments and under different weather conditions. LiDAR provides accurate estimates robust to visual appearances, however, it is often too noisy beyond 30m for fine-grained estimates due to sparse measurements. Conversely, visual-based models give dense predictions at further distances but perform poorly at all ranges when out of training distribution. To address these challenges, we present ALTER, an offroad perception module that adapts-on-the-drive to combine the best of both sensors. Our visual model continuously learns from new near-range LiDAR measurements. This self-supervised approach enables accurate long-range traversability prediction in novel environments without hand-labeling. Results on two distinct real-world offroad environments show up to 52.5% improvement in traversability estimation over LiDAR-only estimates and 38.1% improvement over non-adaptive visual baseline.

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or GPS tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow artistic guidelines specified before flight. In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at: https://youtu.be/ookhHnqmlaU.