user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Move with Affordance Maps

Paper content:
The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles.
Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model dynamic objects (such as other agents) or semantic constraints (such as wet floors or doorways).
Learning-based RL agents are an attractive alternative because they can incorporate both semantic and geometric information, but are notoriously sample inefficient, difficult to generalize to novel settings, and are difficult to interpret.
In this paper, we combine the best of both worlds with a modular approach that {\em learns} a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners.
Specifically, we design an agent that learns to predict a spatial affordance map that elucidates what parts of a scene are navigable through active self-supervised experience gathering.
In contrast to most simulation environments that assume a static world, we evaluate our approach in the VizDoom simulator, using large-scale randomly-generated maps containing a variety of dynamic actors and hazards.
We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
The ability to explore and navigate within a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles.
Traditional approaches for navigation and exploration rely on simultaneous localization and mapping (SLAM) methods to recover scene geometry, producing an explicit geometric map as output.
Such maps can be used in conjunction with classic geometric motion planners for exploration and navigation (such as those based on graph search).
However, geometric maps fail to capture dynamic objects within an environment, such as humans, vehicles, or even other autonomous agents.
In fact, such dynamic obstacles are intentionally treated as outliers to be ignored when learning a geometric map.
However, autonomous agents must follow a navigation policy that avoids collisions with dynamic obstacles to ensure safe operation.
Moreover, real-world environments also offer a unique set of affordances and semantic constraints to each agent: a human-sized agent might fit through a particular door, but a car-sized agent may not; similarly, a bicycle lane may be geometrically free of obstacles, but access is restricted to most agents.
Such semantic and behavioral constraints are challenging to encode with classic SLAM.
One promising alternative is end-to-end reinforcement learning (RL) of a policy for exploration and navigation.
Such approaches have the potential to jointly learn an exploration/navigation planner together with an internal representation that captures both geometric, semantic, and dynamic constraints.
However, such techniques suffer from well-known challenges common to RL such as high sample complexity (because reward signals tend to be sparse), difficulty in generalization to novel environments (due to overfitting), and lack of interpretability.
We advocate a hybrid approach that combines the best of both worlds.
Rather than end-to-end learning of both a spatial representation and exploration policy, we apply learning only "as needed".
Specifically, we employ off-the-shelf planners, but augment the classic geometric map with a spatial affordance map that encodes where the agent can safely move.
Crucially, the affordance map is learned through self-supervised interaction with the environment.
For example, our agent can discover that spatial regions with wet-looking floors are non-navigable and that spatial regions that recently contained human-like visual signatures should be avoided with a large margin of safety.
Evaluating on an exploration-based task, we demonstrate that affordance map-based approaches are far more sample-efficient, generalizable, and interpretable than current RL-based methods.
Even though we believe our problem formulation to be rather practical and common, evaluation is challenging in both the physical world and virtual simulators.
It it notoriously difficult to evaluate real-world autonomous agents over a large and diverse set of environments.
Moreover, many realistic simulators for navigation and exploration assume a static environment (Wu et al., 2018; Savva et al., 2017; Xia et al., 2018) .
We opt for first-person game-based simulators that populate virtual worlds with dynamic actors.
Specifically, we evaluate exploration and navigation policies in VizDoom (Wydmuch et al., 2018) , a popular platform for RL research.
We demonstrate that affordance maps, when combined with classic planners, dramatically outperform traditional geometric methods by 60% and state-of-the-art RL approaches by 70% in the exploration task.
Additionally, we demonstrate that by combining active learning and affordance maps with geometry, navigation performance improves by up to 55% in the presence of hazards.
However, a significant gap still remains between human and autonomous performance, indicating the difficulty of these tasks even in the relatively simple setting of a simulated world.
We have described a learnable approach for exploration and navigation in novel environments.
Like RL-based policies, our approach learns to exploit semantic, dynamic, and even behavioural properties of the novel environment when navigating (which are difficult to capture using geometry alone).
But unlike traditional RL, our approach is made sample-efficient and interpretable by way of a spatial affordance map, a novel representation that is interactively-trained so as to be useful for navigation with off-the-shelf planners.
Though conceptually simple, we believe affordance maps open up further avenues for research and could help close the gap between human and autonomous exploration performance.
For example, the dynamics of moving obstacles are currently captured only in an implicit fashion.
A natural extension is making this explicit, either in the form of a dynamic map or navigability module that makes use of spatio-temporal cues for better affordance prediction.

assistant: text: We address the task of autonomous exploration and navigation using spatial affordance maps that can be learned in a self-supervised manner, these outperform classic geometric baselines while being more sample efficient than contemporary RL algorithms