Venue: CoRL 2023 Conference

Reviews:

final_score: rating: Strong Reject: I recommend rejecting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary_of_paper: This paper presents the T-Dex framework trained with robot play data to learn non-parametric policies that combine the tactile observations with visual ones. The robot play data provides the necessary demonstration that involves high-dimensional tactile data for training skills for manipulation, which the authors achieved through the T-Dex framework in a series of tasks. By comparing with a series of existing methods, the author show that the proposed method is superior in performance in five selected tasks of object manipulation. The authors explored three research questions in experiments and generalized the proposed method to unseen objects. Limitations of this work is also explained briefly and the video demonstration is helpful to understand this work. 
review: While the paper is technically complete in demonstrating the idea, the main issue is probably the innovation behind, which is lacking. It is widely accepted that adding tactile data to visual information helps and improves grasping performances, which is proved again in this work. The involvement of high-dimensional tactile data in this case is no different at all. The robot play data is also well-adopted method in the current literature, although in this particular case it seems it is not exactly the robot playing by itself, but teleoperated by a human operator to play with the robot system during playing. It is unclear what the T-Dex fully represents, the dataset itself, the algorithms developed, or both. Further breakdown statistics of the tasks or objects performed during robot play helps the readers to better understand the nature of this dataset itself. The learning part is also pretty standard and well-explored using Nearest-Neighbor retrieval. 

The three research questions need further improvement. Question 1 is already explored in the currently literature extensively, and proved again in this work. Question 2 is essentially a known conclusion that pre-training improves learning performances, whether it is robot play data or not. And for Question 3, it is interesting to see that a much simpler Alex-net outperforms the other more complexed algorithms, especially after pre-training. While the authors did a lot of experiments with variations on the task and objects, it would be great to focus on a few representative ones with more tests than just 10 trails for a more robust conclusion, given that the authors have already conducted a lot of experiments. 

And finally, the most concerning part is the close resemblance of this paper with another submission, No. 161 See-to-touch paper for CoRL. These two papers used exactly the same setup and share almost the same paper structure (different placement of related works section). Although they are positioned differently, I don't think this should be encouraged, making the work more like a production from paper mill or automatic generation instead of genuine research. 
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are not well addressed
summary_of_recommendation: Overall, I do not recommend this paper for acceptance due to the lack of innovation with repetition of known understandings, limitations in the method and data that is not clear on sharability and reproducibility, and the close resemblance to another submission to CoRL 2023.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary_of_paper: The paper presents a method to learn dexterous manipulation from visuo-tactile data combining techniques of play and offline non-parametric imitation learning. To do this, the authors present a two-phase approach. In stage 1, large amounts of unrestrained play data is collected using teleoperation on a set of objects. Then an encoder is trained on the tactile sensory images using BYOL in a self-supervised fashion. In stage 2, a small number of demonstrations are collected for each task and embedded using the pretrained encoder in stage 1. During test time, the input observations are embedded and actions are outputted based on a nearest-neighbor search on the demonstration embeddings. The results indicate that the play data for tactile representation learning plays a crucial role when combined with visual data for learning dexterous manipulation.
review: ### Strengths:
1.	The proposed method to pretrained tactile representations on play data performs very well in combination with visual representations. The efficacy of the tactile data is demonstrated with a rigorous comparison with non-tactile image-only observations which doesn’t perform as well for the given tasks. Although inspired by prior works using BYOL, the results in the context of using tactile data and the associated analysis are new and useful.
2.	The evaluation conducted is comprehensive and covers a wide range of baselines spanning parametric as well as non-parametric approaches. For the tasks considered, the parametric approaches overfit when provided with limited demonstrations. In this context, the proposed NN based approach adopted by T-Dex serves very useful in generalizing with sparse training data. Amongst the non-parametric methods, image-only and tactile-only are shown to perform worse than T-Dex as expected. 
3.	The analysis of design choices in section 4.4 is an added bonus and the authors have done a great job testing out each component of their proposed method including architecture and tactile input structuring. 
4.	The paper is very well written and is a joy to read. Each section is precise and thorough in its formulation.


### Weaknesses:
1.	Evaluating on more tasks would have added greater insight and impact to the results. Given that the play data was not dependent on successful interactions and took only 2.5 hours for 5 tasks, it seems to be relatively easy to scale to more diverse tasks.
2.	Section describing tactile pad structure is unclear. Refer to rebuttal question.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper demonstrated how tactile play data can be leveraged for pretraining encoders for learning tactile representations using SSL to be later used for offline IL. The evaluation is thorough and the results demonstrate the efficacy of visuo-tactile learning in high-dimensional action spaces.

### Post Rebuttal
The authors have updated the paper according to the provided suggestions and added necessary details about the tactile hardware and observation space. I retain my recommendation of strong accept.
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary_of_paper: This paper presents a self-supervised pre-training learning method for tactile encoders using play data.
This paper uses Holo-Dex framework as a teleoperation framework, Allgro hand as a robot hand, Kinova robot arm, Realsense cameras, and XELA uSkin as a tactile sensor.
In the first phase, this method collects play data and learns tactile representation.
In the second phase, the method collects expert task demonstrations with vision and learns tasks using the nearest neighbor method to select a hand and finger pose. 

The main contribution of the paper is to present a method for self-supervised pre-training of tactile presentation with play data for learning dextrous manipulation.
review: Strenghs
- The paper is written clearly.
- Imitation learning dextrous manipulation, unsupervised learning tactile representation, leveraging play data, and pre-training for learning manipulation are significant issues that the paper tackles.
- The description of the method and experiments sound technical.
- The originality of the paper is to address the pre-training unsupervised tactile representation with play data for learning downstream dexterous manipulation.

Weaknesses
- The downstream tasks, such as opening a book, bottle cap opening, and unstacking cups, do not seem challenging. A simple parallel gripper can execute these tasks. The tasks do not express the dexterity of the multi-finger robot hand. 
- Their nearest neighbor method does not have scalability. Their method may be used for more complex manipulation task which requires continuous control.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper is well-written and has clarity, originality, and significance.
However, the downstream tasks do not seem challenging dexterous manipulation requiring multiple fingers, and it is not unclear whether the proposed method can be used for more complex manipulation.
Therefore, the reviewer recommends weak accept.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary_of_paper: This work presents a method for learning self-supervised tactile encoders from unlabeled play data that are useful in downstream dexterous manipulation tasks.
review: ## Strengths
* Well motivated technique for pretraining tactile representations from unstructured play data. The techniques ability to learn from either successful or unsuccessful demonstrations seems particularly well aligned with the realities of teleoperation for dexterous manipulation studied in this work, where the failure rate of teleoperators is 5/ 6.
* Clear description of the method and helpful and practical descriptions of how the dataset was constructed and filtered.
Strong evidence under extensive real world evaluations (50 hours) of the efficacy of the proposed technique. 
* Good ablations quantifying design decisions and highlighting the importance of pre-training on play, and how separate tasks scale differently with more play.
* (Pending the actual release) The 2.5 hours of high rate vision and tactile sensing play data could be useful to the community as a source of pretraining or for studying multimodal representation learning.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: This work shows evidence that tactile representations learned from large amounts of task-agnostic play data are useful in downstream dexterous imitation learning tasks, and generalize to new visually distinct object instances. This feels like an important finding to communicate to the community, as it derisks a strategy for learning from more scalable forms of supervision than those currently considered for tactile dexterous manipulation.
review_title: null

Meta-review: The paper presents a method for pretraining tactile representations using unstructured play data. When combined with visual observations, this method demonstrates improved performance in fine-grained manipulation tasks through imitation learning, using just a handful of demonstrations.

The overall sentiment from the reviewers regarding this paper is predominantly positive. They recognize the significance of the tasks undertaken, the clarity of the method's description, the breadth of real-world evaluations, the importance of the findings in tactile dexterous manipulation, and the anticipated usefulness of the dataset to the community.

However, some reviewers raised concerns about the novelty of the proposed approach, the actual dexterity of the tested tasks, and the scalability of the nearest neighbor method. While the community broadly acknowledges that tactile information enhances complex contact-rich manipulation tasks and play data can facilitate the learning process, this paper primarily offers a systems contribution. Given the capabilities showcased in the evaluations, the detailed system design, and the key findings, this work can be of great value to the community. As a result, I am inclined towards acceptance.

The authors have promised a number of changes to the final version of the paper. Please make sure to include those in your revision.

Additionally, Reviewer x3zE expressed concerns regarding a perceived similarity between this paper and another submission, No. 161 See-to-touch, also under review at CoRL. As the Area Chair for Paper 49, I do not have access to Paper 161 for a detailed comparison. And the discussion of the resemblance might violate anonymity requirements. The Program Chair recommends handling this concern through a different channel, so the final decision on this issue rests with the PC.
Decision: Accept (Poster)