Venue: CoRL 2022 Conference

Reviews:

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: This paper proposes reducing slip during dynamic motions by adapting the trajectory, specifically decreasing the velocity. This is in contrast to previous papers which normally focus on moderating gripping force. After gathering a dataset of slip experienced while completing linear motions with varying acceleration and deceleration profiles, the paper proposes two LSTM-based models: slip detection and slip prediction. These models are used in the two proposed methods: Reactive Slip Control (RSC) and Proactive Slip Control (PSC). RSC reactively decreases the task space velocity until slip detection gives zero while PSC formulates a trajectory optimization problem where the objective is to minimize expected slip while staying close to reference trajectory. Unsurprisingly PSC outperforms RSC, since it can plan to prevent slip before it occurs. Since trajectories are modeled by linear gaussian parametric models the paper compares how the number of basis functions impacts performance. 
strengths_and_weaknesses: This paper takes an interesting approach to reducing slip by modifying the trajectory rather than the gripping force. The paper argues that in several scenarios, such as manipulating delicate objects, it may be undesirable to simply grip harder. The paper offers both a reactive and proactive formulation to address slip. While the results show that a proactive approach leads to less overall slip, it would be interesting to discuss if there is ever a case where RSC would be preferable to PSC. There is a very interesting discussion in the appendix on when grip force and trajectory adaption might be necessary in combination. 

Both RSC and PSC are well-explained. The video also does an excellent job of communicating the main points of the paper and showing off the results. 

One slight weakness of the paper is that the training set is composed of only one object (with varying weights) and the only trajectories are linear motions (with varying acceleration and deceleration profiles). As shown in the experimental section, this limits the generalizability. In testing on novel objects, the maximum rotation for all objects is above the desired 6 degree threshold. A larger training set could tease out where the generalization issue stems from. 

In considering slip, the paper ignores translational slip, only focusing on rotation slip (defined as a slip of more than 6 degrees). Is translation slip considered less of an issue? Would translation slip be a larger concern for a different class of motions? The presentation of results is more clear when focusing on just rotational slip. However, given the emphasis on dynamic motions, it is a limitation to ignore translational slip. 
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I am weakly recommended that the paper be accepted. The paper presents an interesting alternative to addressing slip and distinguishes itself by considering dynamic trajectories. The two proposed methods (RSC and PSC) are mathematically sound and there are interesting and relevant robotics experiments comparing them and analyzing their parameters. While the proposed methods could be more general, by leveraging a more diverse training set and considering translational slip, I believe the contributions are still compelling and well-demonstrated through the experimental section.  
issues: It could strengthen the paper to discuss how this method could be applied for translational slip and how the system could handle both rotational and translational slip. If possible, it would be great to consider a larger set of training objects/trajectories, although it is not clear that this is feasible in the rebuttal period.

A few more minor notes, questions, comments:
- In Fig. 2 it would be easier to compare the rotations if all the y-axes were the same. Additionally, is it correct to assume that the red line in (d) and (e) corresponds to the reference trajectory?
- How is the time window C chosen for RSC and PSC? How would varying this window impact the results? Likewise, how was the prediction horizon h chosen? (For the experiments it seems h=10). How would varying this impact results?
- The explanation of RSC mentions that formulation was found empirically. Is this referring to the form of the cost function or the constants? What other formulations were considered and why was the presented form selected?
- Is there any intuition on why PSC performs better with a small (2) set of basis functions? In particular, are there thoughts on why RSC needs more (5) basis functions?
- Sec 3 mentions that uSkin can be challenging to calibrate. Given that this was mentioned, did this impact the results in any way and are there tricks that can be done to mitigate this issue?
- With respect to the training set object, it would be helpful to mention, perhaps in the appendix, what weights were used. Is it also correct to assume that the metal pieces were fixed in the box such that they could not move?
- It might be more beneficial to place the data set section after the methodology section, since doing so would provide more context for the data set section.
- On the first page, line 34 there is a missing close paren.
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: The goal of this work is to develop a slip control mechanism for robot manipulation tasks where objects being manipulated may move relative to the gripper due to insufficient shear force. The key idea is to learn a slip prediction model that takes tactile sensor data and planned future actions as input and outputs an estimation of whether the object is going to slip. The learned slip prediction model is then used as a constraint in a trajectory optimization formulation to adjust the planned robot motion in order to proactively prevent slipping. The proposed model is trained with a single object with varying weights and robot motions and evaluated on novel objects. It is also compared to a baseline method of reactive slip control, where the robot motion is adapted after detecting the slip, and the robot motion without considering the slip. The proposed method achieved a comparable success rate compared to reactive slip control, while being able to reduce the object orientation.
strengths_and_weaknesses: Strengths
- The idea of learning a predictive model for preventing slipping during manipulation is interesting and the proposed algorithm seems concrete.
- The collected dataset for slipping prediction/detection could be of interest to researchers in relevant fields.
- The result is demonstrated on real, physical robots with non-trivial manipulation tasks.

Weaknesses
- The advantage over reactive slip control doesn’t seem a lot
- The performance of the proposed method (PSC) seems to get worse as the number of basis functions increases. This raises a concern for the ability of the algorithm to be applied to more complex tasks where a higher-order motion is needed.
- The designed customized optimality is not very straightforward. I think it’s more clear and simpler if they separately report the slipping events and the tracking performance, instead of merging them with some weights alpha.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: As mentioned in the weaknesses above, my main concerns for the work is that it didn't show much advantage over the reactive slip control, which could also use some additional tweaking, and it's not clear how well it can be extended to more complex tasks given the experiment results. That being said, I think the idea of learning to proactively prevent slipping during manipulation is interesting, the dataset generated and the designed task in this work may inspire future research in this area. Thus I'm currently leaning towards accept.
issues: - What's the range of weights seen during data collection? It is mentioned that metal pieces are added to the box but I didn't find how many of them.
- How would the reactive slip control perform is a buffer is used, e.g. if slip is defined as exceed 6 degrees in orientation, the detection model can be trained to trigger when the orientation exceeds 4 degrees. The paper mentioned that RSC can already track the reference motion better, if setting the threshold more conservatively also improves orientation control it may perform at least as good as the proactive one?
- What are the statistics for the two terms in the customized optimality separately? It is mentioned that RSC tracks the motion better, which corresponds to the first term. Does it mean that slip happens more often in RSC?
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: This paper applied machine learning to the problem of slip detection and avoidance in manipulation tasks. They implement two solutions: one a purely reactive method which uses an LSTM to detect slip events triggering the controller to slow it's velocity. The second implementation is also an LSTM but takes in both past data from the tactile sensor and future planned actions from the controller to predict slip. These future predictions are then used to re-optimize the planned trajectory to pre-emptively avoid slip. 
strengths_and_weaknesses: Strengths:
- Good application of learning. Slip is very difficult to model and learning methods are becoming more common for this purpose.
- Well justified choices in network and problem formulation

Weaknesses:
- The paper is unclear on some important information such as experimental setup.
- I generally find the paper is written in a confusing way with implementation details in the beginning and theory later resulting in a lot of back and forth reading.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I think this paper is a solid idea with good implementation. The weakest parts are in the writing of the paper itself which I expect the authors can improve. I have listed my issues with the paper in it's current form below.
issues: - The biggest issue with this paper is that the experimental setup and reference trajectory is not explained. It was not clear at all why rotation was used to estimate slip until watching the video. The authors should clearly explain the goal trajectory used in the experiments and how exactly does the object slip out of the robot's grasp. From looking at Figure 1, I assumed the robot just lifted the box in the air which made the entire paper very confusing.
- The text in Figure 1 is too small and blurry. Also adding labels for all the signals would make it more clear. For example writing "actions" and "tactile data" next to the arrows coming from the robot.
- I find the order of the paper confusing, perhaps the datasets section should go after Methodology.
- Figure 3 should include the object names either in the figure or caption so readers can know which object is theBest vs THINS for example.
- Some important implementation details are missing such as planning horizon and tactile data window length.
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: The paper develops a novel algorithm for contact slip avoidance in robotic manipulation of objects. The algorithms consists of an offline procedure of learning a slid detection and prediction model and online trajectory adaptation to prevent slip. Several experiments are conducted. 


strengths_and_weaknesses: Strengths:
- The paper deals with an interesting and relevant problem. 
- The proposed method seems to be novel with respect to the related literature. 
- There are numerous experimental results to test the method.

Weaknesses: 
- In some parts, the paper lacks clarity and good structure. The most important is that the overall algorithm is unclear from a first readthrough.  I understand that two models are trained using offline trajectories, one for slid detection and one for slip prediction. Then these models are used in the online optimization process. However, this is not explicitly mentioned in the paper.  
- Since there are not dynamics taken into account and no theoretical guarantees, the proposed algorithm should be tested with other state-of-the-art algorithms on slip prevention. 


robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper seems to propose a novel algorithm, but it needs further revision on the structure and presentation as well as the experimental results.
issues: The issues to be addressed consist of:
-  Lack of good flow and structure, I believe a section before Section 3. summarizing the flow of the proposed algorithm, breaking down the offline learning and online optimization would be very beneficial.
- Lack of comparative experiments with respect to related works. 

Further, why is the robot's action parameterized by a Gaussian parametric model? Why not just consider the robot action as the optimization variable? Similarly, the notation $\dot{X}$ (without subscripts) seems to be undefined. 

The paper should be revised with respect to some grammatical mistakes.
review_title: null

Meta-review: metareview: This paper presents a method for preventing slips in point-to-point motions while grasping an object using LSTM-based models for detecting and predicting slips. This approach is effective when contact force cannot be increased. Reactive and proactive controllers are presented, and the latter demonstrated better performance. 

The paper proposes an interesting approach to slip prevention especially useful for handling delicate objects. The authors successfully addressed most of the concerns raised by the reviewers, while expanding the dataset and examples will be addressed in a future journal paper. Nevertheless, the paper will make a valuable contribution to the conference.
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)