Neural Probabilistic Motor Primitives for Humanoid Control
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.
 The paper tackles the problem of distilling large numbers of expert demonstrations into a single policy that can both recreate original demonstrations in a physically-simulated environment and humanoid platform, and to generalize to novel motions. Towards this, the paper presents two approaches learn policies from expert demonstrations without involving costly closed loop RL training, and distilling these individual experts into a shared policy by learning latent time-varying codes. The paper is well-written and the method is well-evaluated in the scope that it is proposed. Both components of the proposed approach have previously been explored in the literature - there is extensive work on learning local controllers for physics based evironments from demonstrations in both open loop and closed loop settings as well as work on mixtures of these controllers in machine learning, robotics and computer graphics communities. While the paper proposes these two components as a contribution, I would like to see a more detailed argument of what this work contributes over previous such approaches. Another part where I wish the paper could make a more compelling argument is that distilled policy can perform non-trivial generalization. Target following is a good illustrative example, but has been showcased by multitude of prior work. The paper talks about compositionality, and it would have been compelling to see examples of that if the method can achieve it. For example, simultaneously performing locomotion skills with upper body manipulation skills is something mixture of expert demonstrations approaches still struggle with and it would have been great to see this paper investigate the approach on this problem. Overall, this is a sound and well-written submission, but the existence of very related prior work with similar capabilities makes me reluctant to recommend this paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Probabilistic Motor Primitives for Humanoid Control
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space. The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories. Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic. To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning. We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.
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 paper tackles the problem of distilling large numbers of expert demonstrations into a single policy that can both recreate original demonstrations in a physically-simulated environment and humanoid platform, and to generalize to novel motions. Towards this, the paper presents two approaches learn policies from expert demonstrations without involving costly closed loop RL training, and distilling these individual experts into a shared policy by learning latent time-varying codes. The paper is well-written and the method is well-evaluated in the scope that it is proposed. Both components of the proposed approach have previously been explored in the literature - there is extensive work on learning local controllers for physics based evironments from demonstrations in both open loop and closed loop settings as well as work on mixtures of these controllers in machine learning, robotics and computer graphics communities. While the paper proposes these two components as a contribution, I would like to see a more detailed argument of what this work contributes over previous such approaches. Another part where I wish the paper could make a more compelling argument is that distilled policy can perform non-trivial generalization. Target following is a good illustrative example, but has been showcased by multitude of prior work. The paper talks about compositionality, and it would have been compelling to see examples of that if the method can achieve it. For example, simultaneously performing locomotion skills with upper body manipulation skills is something mixture of expert demonstrations approaches still struggle with and it would have been great to see this paper investigate the approach on this problem. Overall, this is a sound and well-written submission, but the existence of very related prior work with similar capabilities makes me reluctant to recommend this paper.
assistant
 The paper tackles the problem of distilling large numbers of expert demonstrations into a single policy that can both recreate original demonstrations in a physically-simulated environment and humanoid platform, and to generalize to novel motions. Towards this, the paper presents two approaches learn policies from expert demonstrations without involving costly closed loop RL training, and distilling these individual experts into a shared policy by learning latent time-varying codes. The paper is well-written and the method is well-evaluated in the scope that it is proposed. Both components of the proposed approach have previously been explored in the literature - there is extensive work on learning local controllers for physics based evironments from demonstrations in both open loop and closed loop settings as well as work on mixtures of these controllers in machine learning, robotics and computer graphics communities. While the paper proposes these two components as a contribution, I would like to see a more detailed argument of what this work contributes over previous such approaches. Another part where I wish the paper could make a more compelling argument is that distilled policy can perform non-trivial generalization. Target following is a good illustrative example, but has been showcased by multitude of prior work. The paper talks about compositionality, and it would have been compelling to see examples of that if the method can achieve it. For example, simultaneously performing locomotion skills with upper body manipulation skills is something mixture of expert demonstrations approaches still struggle with and it would have been great to see this paper investigate the approach on this problem. Overall, this is a sound and well-written submission, but the existence of very related prior work with similar capabilities makes me reluctant to recommend this paper.