AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking
Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. Previous work has shown that the tracker can be trained in a simulator via reinforcement learning and deployed in real-world scenarios. However, during training, such a method requires manually specifying the moving path of the target object to be tracked, which cannot ensure the tracker’s generalization on the unseen object moving patterns. To learn a robust tracker for VAT, in this paper, we propose a novel adversarial RL method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In AD-VAT, both the tracker and the target are approximated by end-to-end neural networks, and are trained via RL in a dueling/competitive manner: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. They are asymmetric in that the target is aware of the tracker, but not vice versa. Specifically, besides its own observation, the target is fed with the tracker’s observation and action, and learns to predict the tracker’s reward as an auxiliary task. We show that such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To stabilize the training, we also propose a novel partial zero-sum reward for the tracker/target. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. For supplementary videos, see: https://www.youtube.com/playlist?list=PL9rZj4Mea7wOZkdajK1TsprRg8iUf51BS The code is available at https://github.com/zfw1226/active_tracking_rl
 This is in a visual active tracking application. The paper proposes a novel reward function - "partial zero sum", which only encourages the tracker-target competition when they are close and penalizes whey they are too far. This is a very interesting problem and I see why their contribution could improve the system performance. Clarity: the paper is well-written. I also like how the author provides both formulas and a lot of details on implementation of the end-to-end system. Originality: Most of the components are pretty standard, however I value the part that seems pretty novel to me - which is the "partial zero-sum" idea. Evaluation: the result obtained from the simulated environment in 2d and 3d are convincing. However, if 1) real-world test and results 2) a stronger baseline can be used, that would be a stronger acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking
Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. Previous work has shown that the tracker can be trained in a simulator via reinforcement learning and deployed in real-world scenarios. However, during training, such a method requires manually specifying the moving path of the target object to be tracked, which cannot ensure the tracker’s generalization on the unseen object moving patterns. To learn a robust tracker for VAT, in this paper, we propose a novel adversarial RL method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In AD-VAT, both the tracker and the target are approximated by end-to-end neural networks, and are trained via RL in a dueling/competitive manner: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. They are asymmetric in that the target is aware of the tracker, but not vice versa. Specifically, besides its own observation, the target is fed with the tracker’s observation and action, and learns to predict the tracker’s reward as an auxiliary task. We show that such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To stabilize the training, we also propose a novel partial zero-sum reward for the tracker/target. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. For supplementary videos, see: https://www.youtube.com/playlist?list=PL9rZj4Mea7wOZkdajK1TsprRg8iUf51BS The code is available at https://github.com/zfw1226/active_tracking_rl
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 is in a visual active tracking application. The paper proposes a novel reward function - "partial zero sum", which only encourages the tracker-target competition when they are close and penalizes whey they are too far. This is a very interesting problem and I see why their contribution could improve the system performance. Clarity: the paper is well-written. I also like how the author provides both formulas and a lot of details on implementation of the end-to-end system. Originality: Most of the components are pretty standard, however I value the part that seems pretty novel to me - which is the "partial zero-sum" idea. Evaluation: the result obtained from the simulated environment in 2d and 3d are convincing. However, if 1) real-world test and results 2) a stronger baseline can be used, that would be a stronger acceptance.
assistant
 This is in a visual active tracking application. The paper proposes a novel reward function - "partial zero sum", which only encourages the tracker-target competition when they are close and penalizes whey they are too far. This is a very interesting problem and I see why their contribution could improve the system performance. Clarity: the paper is well-written. I also like how the author provides both formulas and a lot of details on implementation of the end-to-end system. Originality: Most of the components are pretty standard, however I value the part that seems pretty novel to me - which is the "partial zero-sum" idea. Evaluation: the result obtained from the simulated environment in 2d and 3d are convincing. However, if 1) real-world test and results 2) a stronger baseline can be used, that would be a stronger acceptance.