Learning to Move with Affordance Maps
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.
This paper presents an approach for navigating and exploring in environments with dynamic and environmental hazards that combines geometric and semantic affordance information in a map used for path planning. Overall this paper is fairly well written. Results in a VizDoom testbed show favorable performance compared to both frontier and RL baselines, and the author*s approach is more sample-ef?cient and generalizable than RL-based approaches. I wouldn*t consider any particular aspect of this paper to be that novel, but it is a nice combination of leveraging active self-supervised learning to generate spatial affordance information for fusion with a geometric planner. As humans show the best performance on the tasks, it might be worth considering learning a policy from human demonstrations through an imitation learning approach.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Move with Affordance Maps
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents an approach for navigating and exploring in environments with dynamic and environmental hazards that combines geometric and semantic affordance information in a map used for path planning. Overall this paper is fairly well written. Results in a VizDoom testbed show favorable performance compared to both frontier and RL baselines, and the author*s approach is more sample-ef?cient and generalizable than RL-based approaches. I wouldn*t consider any particular aspect of this paper to be that novel, but it is a nice combination of leveraging active self-supervised learning to generate spatial affordance information for fusion with a geometric planner. As humans show the best performance on the tasks, it might be worth considering learning a policy from human demonstrations through an imitation learning approach.
assistant
This paper presents an approach for navigating and exploring in environments with dynamic and environmental hazards that combines geometric and semantic affordance information in a map used for path planning. Overall this paper is fairly well written. Results in a VizDoom testbed show favorable performance compared to both frontier and RL baselines, and the author*s approach is more sample-ef?cient and generalizable than RL-based approaches. I wouldn*t consider any particular aspect of this paper to be that novel, but it is a nice combination of leveraging active self-supervised learning to generate spatial affordance information for fusion with a geometric planner. As humans show the best performance on the tasks, it might be worth considering learning a policy from human demonstrations through an imitation learning approach.