Learning Awareness Models
We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent*s body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent*s own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.
 The authors explore how sequence models that look at proprioceptive signals from a simulated or real-world robotic hand can be used to decode properties of objects (which are not directly observed), or produce entropy maximizing or minimizing motions. The overall idea presented in the paper is quite nice: proprioception-based models that inject actions and encoder/pressure observations can be used to measure physical properties of objects that are not directly observed, and can also be used to create information gathering (or avoiding) behaviors. There is some related work that the authors do not cite that is highly relevant here. A few in particular come to mind: Yu, Tan, Liu, Turk. Preparing for the Unknown: uses a sequence model to estimate physical properties of a robot (rather than unobserved objects) Fu, Levine, Abbeel. One-Shot Learning of Manipulation Skills: trains a similar proprioception-only model and uses it for object manipulation, similar idea that object properties can be induced from proprioception But in general the citations to relevant robotic manipulation work are pretty sparse. The biggest issue with the paper though is with the results. There are no comparisons or reasonable baselines of any kind, and the reported results are a bit hard to judge. As far as I can understand, there are no quantitative results in simulation at all, and the real-world results are not good, indicating something like 15 degrees of error in predicting the pose of a single object. That doesn*t seem especially good, though it*s also very hard to tell without a baseline. Overall, this seems like a good workshop paper, but probably substantial additional experimental work is needed in order to evaluate the practical usefulness of this method. I would however strongly encourage the authors to pursue this research further: it seems very promising, and I think that, with more rigorous evaluation and comparisons, it could be quite a nice paper! One point about style: I found the somewhat lofty claims in the introduction a bit off-putting. It*s great to discuss the greater "vision" behind the work, but this paper suffers from a bit too much high-level vision and not enough effort put into explaining what the method actually does.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Awareness Models
We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent*s body come to represent objects in the external world. In spite of being trained with only internally available signals, these dynamic body models come to represent external objects through the necessity of predicting their effects on the agent*s own body. That is, the model learns holistic persistent representations of objects in the world, even though the only training signals are body signals. Our dynamics model is able to successfully predict distributions over 132 sensor readings over 100 steps into the future and we demonstrate that even when the body is no longer in contact with an object, the latent variables of the dynamics model continue to represent its shape. We show that active data collection by maximizing the entropy of predictions about the body---touch sensors, proprioception and vestibular information---leads to learning of dynamic models that show superior performance when used for control. We also collect data from a real robotic hand and show that the same models can be used to answer questions about properties of objects in the real world. Videos with qualitative results of our models are available at https://goo.gl/mZuqAV.
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>



 The authors explore how sequence models that look at proprioceptive signals from a simulated or real-world robotic hand can be used to decode properties of objects (which are not directly observed), or produce entropy maximizing or minimizing motions. The overall idea presented in the paper is quite nice: proprioception-based models that inject actions and encoder/pressure observations can be used to measure physical properties of objects that are not directly observed, and can also be used to create information gathering (or avoiding) behaviors. There is some related work that the authors do not cite that is highly relevant here. A few in particular come to mind: Yu, Tan, Liu, Turk. Preparing for the Unknown: uses a sequence model to estimate physical properties of a robot (rather than unobserved objects) Fu, Levine, Abbeel. One-Shot Learning of Manipulation Skills: trains a similar proprioception-only model and uses it for object manipulation, similar idea that object properties can be induced from proprioception But in general the citations to relevant robotic manipulation work are pretty sparse. The biggest issue with the paper though is with the results. There are no comparisons or reasonable baselines of any kind, and the reported results are a bit hard to judge. As far as I can understand, there are no quantitative results in simulation at all, and the real-world results are not good, indicating something like 15 degrees of error in predicting the pose of a single object. That doesn*t seem especially good, though it*s also very hard to tell without a baseline. Overall, this seems like a good workshop paper, but probably substantial additional experimental work is needed in order to evaluate the practical usefulness of this method. I would however strongly encourage the authors to pursue this research further: it seems very promising, and I think that, with more rigorous evaluation and comparisons, it could be quite a nice paper! One point about style: I found the somewhat lofty claims in the introduction a bit off-putting. It*s great to discuss the greater "vision" behind the work, but this paper suffers from a bit too much high-level vision and not enough effort put into explaining what the method actually does.
assistant
 The authors explore how sequence models that look at proprioceptive signals from a simulated or real-world robotic hand can be used to decode properties of objects (which are not directly observed), or produce entropy maximizing or minimizing motions. The overall idea presented in the paper is quite nice: proprioception-based models that inject actions and encoder/pressure observations can be used to measure physical properties of objects that are not directly observed, and can also be used to create information gathering (or avoiding) behaviors. There is some related work that the authors do not cite that is highly relevant here. A few in particular come to mind: Yu, Tan, Liu, Turk. Preparing for the Unknown: uses a sequence model to estimate physical properties of a robot (rather than unobserved objects) Fu, Levine, Abbeel. One-Shot Learning of Manipulation Skills: trains a similar proprioception-only model and uses it for object manipulation, similar idea that object properties can be induced from proprioception But in general the citations to relevant robotic manipulation work are pretty sparse. The biggest issue with the paper though is with the results. There are no comparisons or reasonable baselines of any kind, and the reported results are a bit hard to judge. As far as I can understand, there are no quantitative results in simulation at all, and the real-world results are not good, indicating something like 15 degrees of error in predicting the pose of a single object. That doesn*t seem especially good, though it*s also very hard to tell without a baseline. Overall, this seems like a good workshop paper, but probably substantial additional experimental work is needed in order to evaluate the practical usefulness of this method. I would however strongly encourage the authors to pursue this research further: it seems very promising, and I think that, with more rigorous evaluation and comparisons, it could be quite a nice paper! One point about style: I found the somewhat lofty claims in the introduction a bit off-putting. It*s great to discuss the greater "vision" behind the work, but this paper suffers from a bit too much high-level vision and not enough effort put into explaining what the method actually does.