Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills
Learning useful and reusable skill, or sub-task primitives, is a long-standing problem in sensorimotor control. This is challenging because it*s hard to define what constitutes a useful skill. Instead of direct manual supervision which is tedious and prone to bias, in this work, our goal is to extract reusable skills from a collection of human demonstrations collected directly for several end-tasks. We propose a weakly-supervised approach for trajectory segmentation following the classic work on multiple instance learning. Our approach is end-to-end trainable, works directly from high-dimensional input (e.g., images) and only requires the knowledge of what skill primitives are present at training, without any need of segmentation or ordering of primitives. We evaluate our approach via rigorous experimentation across four environments ranging from simulation to real world robots, procedurally generated to human collected demonstrations and discrete to continuous action space. Finally, we leverage the generated skill segmentation to demonstrate preliminary evidence of zero-shot transfer to new combinations of skills. Result videos at https://sites.google.com/view/trajectory-segmentation/
This paper introduces a weakly supervised learning approach for trajectory segmentation, which relies on coarse labelling about the occurrence of a skill in a demonstration to segment trajectories. This is accomplished through a recurrent model predicting skill categories for each step in a trajectory, and a trajectory level loss function that penalises the probability of seeing a given skill in a trajectory. Overall, I like the idea of identifying skills in this manner, and think that this is an important problem to address. However, I have concerns about it*s feasibility when a large number of skills are present. It seems that there are certain requirements of skill occurrences in datasets that need to be met if this approach is to be feasible. For example, consider the dataset of skill sequences: [111 222 333] [444 222 333] [222 333 555] It seems that it will never be possible to learn to identify skills 2 and 3 from this dataset. This paper would be greatly strengthened if the minimum dataset requirements to learn all skills were enumerated, or some theoretical bounds provided around when this loose labelling could possibly be successful provided. The paper glosses over this point by suggesting that *data* makes this a non-issue, but the paper would be much stronger if these limitations were confronted and some bounds on the chances of meeting the requirements needed for learning provided. The classification results seem extremely poor, and it is hard to assess these on the basis of accuracy alone. For example, in the Robosuit example, the test results could potentially have been obtained by simply making the same prediction throughout the test, and there is no indication that anything sensible was actually learned. Confusion matrices, or precision and recall metrics, are required to avoid this. At present it is impossible to know if these errors are due to noisy predictions, the dataset limitations described above, or simply a poor classifier. Along these lines, qualitative results in the form of trajectory classification (say following the presentation conventions in Bolanos, 15, https://arxiv.org/pdf/1505.01130.pdf Ranchod, 15, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353414&tag=1 would also help to address these concerns. A question regarding the dial jaco videos experiments, why does the classifier not predict a 5 when moving from 4 to 6? I would expect a very jumpy prediction here, but the prediction looks very smooth - is this a filtered result? Finally, baseline experiments are limited to other weakly supervised learning segmentation approaches, but I think comparisons with unsupervised clustering methods would also be useful. Unfortunately, due to the lack of evidence that the proposed approach is able to learn effectively, I am inclined to reject this paper. Clarifying the bounds, and presenting stronger evidence (beyond accuracy) would make this paper stronger.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills
Learning useful and reusable skill, or sub-task primitives, is a long-standing problem in sensorimotor control. This is challenging because it*s hard to define what constitutes a useful skill. Instead of direct manual supervision which is tedious and prone to bias, in this work, our goal is to extract reusable skills from a collection of human demonstrations collected directly for several end-tasks. We propose a weakly-supervised approach for trajectory segmentation following the classic work on multiple instance learning. Our approach is end-to-end trainable, works directly from high-dimensional input (e.g., images) and only requires the knowledge of what skill primitives are present at training, without any need of segmentation or ordering of primitives. We evaluate our approach via rigorous experimentation across four environments ranging from simulation to real world robots, procedurally generated to human collected demonstrations and discrete to continuous action space. Finally, we leverage the generated skill segmentation to demonstrate preliminary evidence of zero-shot transfer to new combinations of skills. Result videos at https://sites.google.com/view/trajectory-segmentation/
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 introduces a weakly supervised learning approach for trajectory segmentation, which relies on coarse labelling about the occurrence of a skill in a demonstration to segment trajectories. This is accomplished through a recurrent model predicting skill categories for each step in a trajectory, and a trajectory level loss function that penalises the probability of seeing a given skill in a trajectory. Overall, I like the idea of identifying skills in this manner, and think that this is an important problem to address. However, I have concerns about it*s feasibility when a large number of skills are present. It seems that there are certain requirements of skill occurrences in datasets that need to be met if this approach is to be feasible. For example, consider the dataset of skill sequences: [111 222 333] [444 222 333] [222 333 555] It seems that it will never be possible to learn to identify skills 2 and 3 from this dataset. This paper would be greatly strengthened if the minimum dataset requirements to learn all skills were enumerated, or some theoretical bounds provided around when this loose labelling could possibly be successful provided. The paper glosses over this point by suggesting that *data* makes this a non-issue, but the paper would be much stronger if these limitations were confronted and some bounds on the chances of meeting the requirements needed for learning provided. The classification results seem extremely poor, and it is hard to assess these on the basis of accuracy alone. For example, in the Robosuit example, the test results could potentially have been obtained by simply making the same prediction throughout the test, and there is no indication that anything sensible was actually learned. Confusion matrices, or precision and recall metrics, are required to avoid this. At present it is impossible to know if these errors are due to noisy predictions, the dataset limitations described above, or simply a poor classifier. Along these lines, qualitative results in the form of trajectory classification (say following the presentation conventions in Bolanos, 15, https://arxiv.org/pdf/1505.01130.pdf Ranchod, 15, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353414&tag=1 would also help to address these concerns. A question regarding the dial jaco videos experiments, why does the classifier not predict a 5 when moving from 4 to 6? I would expect a very jumpy prediction here, but the prediction looks very smooth - is this a filtered result? Finally, baseline experiments are limited to other weakly supervised learning segmentation approaches, but I think comparisons with unsupervised clustering methods would also be useful. Unfortunately, due to the lack of evidence that the proposed approach is able to learn effectively, I am inclined to reject this paper. Clarifying the bounds, and presenting stronger evidence (beyond accuracy) would make this paper stronger.
assistant
This paper introduces a weakly supervised learning approach for trajectory segmentation, which relies on coarse labelling about the occurrence of a skill in a demonstration to segment trajectories. This is accomplished through a recurrent model predicting skill categories for each step in a trajectory, and a trajectory level loss function that penalises the probability of seeing a given skill in a trajectory. Overall, I like the idea of identifying skills in this manner, and think that this is an important problem to address. However, I have concerns about it*s feasibility when a large number of skills are present. It seems that there are certain requirements of skill occurrences in datasets that need to be met if this approach is to be feasible. For example, consider the dataset of skill sequences: [111 222 333] [444 222 333] [222 333 555] It seems that it will never be possible to learn to identify skills 2 and 3 from this dataset. This paper would be greatly strengthened if the minimum dataset requirements to learn all skills were enumerated, or some theoretical bounds provided around when this loose labelling could possibly be successful provided. The paper glosses over this point by suggesting that *data* makes this a non-issue, but the paper would be much stronger if these limitations were confronted and some bounds on the chances of meeting the requirements needed for learning provided. The classification results seem extremely poor, and it is hard to assess these on the basis of accuracy alone. For example, in the Robosuit example, the test results could potentially have been obtained by simply making the same prediction throughout the test, and there is no indication that anything sensible was actually learned. Confusion matrices, or precision and recall metrics, are required to avoid this. At present it is impossible to know if these errors are due to noisy predictions, the dataset limitations described above, or simply a poor classifier. Along these lines, qualitative results in the form of trajectory classification (say following the presentation conventions in Bolanos, 15, https://arxiv.org/pdf/1505.01130.pdf Ranchod, 15, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353414&tag=1 would also help to address these concerns. A question regarding the dial jaco videos experiments, why does the classifier not predict a 5 when moving from 4 to 6? I would expect a very jumpy prediction here, but the prediction looks very smooth - is this a filtered result? Finally, baseline experiments are limited to other weakly supervised learning segmentation approaches, but I think comparisons with unsupervised clustering methods would also be useful. Unfortunately, due to the lack of evidence that the proposed approach is able to learn effectively, I am inclined to reject this paper. Clarifying the bounds, and presenting stronger evidence (beyond accuracy) would make this paper stronger.