Venue: CoRL 2021 Conference

Reviews:

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: This paper proposes to guide an RL algorithm for grasping tabletop household objects with a hand pose prior derived from in-the-wild images of humans using those objects. Experiments performed in simulation show that this accelerates the learning process and makes the grasps more functional and stable.
strengths_and_weaknesses: # Strengths
- The paper is very well written and was a joy to read!
- It presents an interesting new idea - guiding grasping algorithms with YouTube video data - and evaluates it thoroughly (albeit only in simulation).
- Models the grasping activity as a series of actions rather than the end state only (which is the formulation used by most grasping papers).
- The affordance input can also be manually generated at test time to "steer" the grasp towards a desired area of the object.
- Code will be publicly released.

# Clarification
- How is demo time calculated in Fig 6? Is it the total length of all videos from which images are chosen for DexVIP?

# Weaknesses
- Lack of real world robotic experiments is an obvious weakness, but I don't think that alone should be grounds for rejection this year.
- Effort spent in selecting an appropriate demo image may be quite high. L307 mentions that a demonstration image is collected in a few seconds. But that seems unrealistic, given that an image has to be chosen where the object is upright, the grasp is complete (not approaching), good visibility for FrankMocap, etc. Automating this process, especially detecting when the grasp is complete, might not be as straightforward as claimed. Please add some discussion and visualizations on if the proposed clustering method allows some of these considerations to be relaxed. Why not get the grasp pose from a virtual application that allows the Adroit hand joints to be interactively changed by a user (sort of like virtual kinesthetic teaching)? The paper discusses that normal kinesthetic teaching disembodies the demonstrator, but inaccuracies at a similar level might also be expected from image-based 3D pose estimation.
- It has been noted in the literature that sim2real transfer with RGB image inputs is difficult. Have you considered a policy which does not use RGB images i.e. depth + affordance only?

# Post Rebuttal
I will keep my "strong accept" rating because the rebuttal addresses my concerns. Especially the verification that the lack of RGB image input does not affect performance.
summary_of_recommendation: This paper introduces and novel idea and evaluates it as thoroughly as possible in simulation. The lack of real robot experiments and discussion on alternative demonstration methods like virtual kinesthetic teaching are drawbacks, but in my opinion not enough to warrant rejection.
issues: - Please discuss virtual kinesthetic teaching and challenges likely to be faced while performing sim2real transfer of the policy.
- If possible, show (preliminary) results with a policy that does not rely on RGB image input, because that modality might cause issues with sim2real transfer to a real robot.
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 work proposes an approach to learn dexterous robotic grasping from human-object interactions recorded in the video. Target hand poses are predicted from the video leveraging an existing method. The distances between target hand poses and current hand poses are used as a reward signal to train a deep reinforcement learning algorithm in the simulation. Besides the target hand poses, the affordance of each object is pre-calculated. The distances between the hand and affordance regions are also used as a reward signal. The proposed pipeline enables a 30-Dof hand to grasp 27 objects in the simulation achieving 68% success rates. 
strengths_and_weaknesses: Strengths:
1) This work proposes to leverage the people's hand poses from the video to train dexterous robotic grasping policy.
2) Extensive comparison experiments are conducted to demonstrate the effectiveness of the proposed approaches.
3) The paper is well-organized and easy to follow. 

Weaknesses:
1) Some important information is missing. a) how to calculate the difference between the target hand poses and the current hand poses since the target hand poses returned from the FrankMocap are based on a different hand.  b) how to determine whether there are objects in the video dataset which are similar to the objects that needed grasping.
2) The sim2 real gap remains unclear. What mass values are assigned to these objects? What friction coefficients are used? Are these values sensitive in the learned policy?
3) The comparison experiments on functionality and posture are kind of unfair. Because these attributes are used during the training process. For example, the proposed method is optimized to grasp under similar postures. Of course, it achieves the highest score in the posture metric.  
4) It's unclear that how the prediction errors of the target pose affect the learned policy and how the similarity between the object in the video and the object to be grasped affects the learned policy. 
 
summary_of_recommendation: This work proposes to leverage the people's hand poses from the video to train dexterous robotic grasping policy. The proposed pipeline enables a 30 DoF hand to grasp 27 objects in the simulation with 68% grasp success rates. However, some important explanations of the proposed method are missing. It's unclear how the errors in the human pose prior affect the grasping performances. I would be happy to increase the evaluation score if these concerns are addressed.
issues: 1) In Line 137, "Additionally, we provide a binary affordance map At that is inferred from I0 using an affordance", Is the affordance map inferred from "I_0" or "I_t"
2) In Line 141, "is the pairwise distance between the object affordance regions and contact points on the hand". How are these affordance regions defined? How to calculate the distance between a region on the object with a contact point on the hand? Are the number of regions varying between different objects? 
3) In Line 189-190, "we curate an object interaction repository Ih of 715 video frames from HowTo100M where the human hand is grasping one of the 27 total objects, to yield on average 26 grasp images per object." The object in the video and the object for the simulation are not exactly identical. How to make sure that the object in the video and the object for the simulation are similar enough? Are they verified automatically or by human experts?
4) In Line 197-200, "In particular, we employ FrankMocap [44] to estimate 3D human hand poses (see Fig. 3, left). FrankMocap is a near real-time method for 3D hand and body pose estimation from monocular video; it returns 3D joint angles for detected hands in each frame"
In FrankMocap[40], the 3D joints angles are based on the SMPL-X hand model (Expressive body capture: 3d hands, face,
and body from a single image. Pavlakos et al., CVPR 2019).  How to make sure that the designs and joints of SMPL-X hand model are similar to the hand used in the Mujoco simulation? There are 20 joints in SMPL-X hand while there are 24 joints in the hand used in this work. How to calculate the similarity between the poses of two hands?
5) In Line 209-210, "we next apply k-medoid clustering on each set P(c). We consider the medoid hand pose of the largest cluster to be the consensus target hand pose" What metric space is used for applying k-medoid clustering here?
6) In Line 224-225, "For Rpose, we compute the mean per-joint angle error at time t between the robot joints prt and the target grasp pose". A similar question is in Issue 4).  The two hands have different joint designs. How to measure their error?
7) In Ablation, how do the prediction errors of the target pose affect the learned policy? how does the similarity between the object in the video and the object needed grasping affect the learned policy?
8) In Ablation, why only success rates are reported? How about the rest three metrics including stability, functionality and posture?
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 presents DexVIP, a method for learning a policy for dexterous grasping. Grasping with a dexterous, multi-fingered robot hand is a challenging problem; while the hand’s high degrees of freedom (DOF) make it possible to perform behaviors that are beyond simple parallel-jaw grippers, the large action space presents significant learning challenges. The key contribution of DexVIP is to use human hand pose priors when training the grasping policy, to improve sample complexity and produce human-like grasps. Hand pose priors are first extracted using FrankMocap [1] from in-the-wild video demonstrations of humans grasping an object similar to the desired object. By using video demonstrations, DexVIP does not require expensive or difficult-to-use hardware for collecting demonstrations. The human hand pose priors are incorporated as the “R_pose” term in the reward function (Eq. 1) to encourage the robot hand to use the same grasp pose as the human demonstrator. The method is otherwise identical to GRAFF [2], a state-of-the-art dexterous grasping approach. The paper presents experiments with a simulated 30-DOF hand evaluating DexVIP against a number of baselines: GRAFF, a hybrid imitation+RL approach named DAPG [3], and alternative reward terms such as grasping towards the object center of mass and rewarding contact with the object. The paper also provides additional experiments comparing video-based vs. teleoperation-based demonstrations, ablations, and the effect of training with noise. 
strengths_and_weaknesses: Strengths
- The proposed method helps pave the way towards dexterous robot manipulation
- The data used for training the pose prior can be collected from in-the-wild videos and is hence easy to collect and easily scalable. This data involves people directly interacting with objects, as opposed to the more indirect demonstration approaches of VR, teleoperation, or kinesthetic teaching.
- The proposed method is somewhat invariant to viewpoint variation, since the reward focuses on the pose of the individual hand joints (regardless of the position / orientation of the hand itself)
- The paper evaluates DexVIP against existing methods and shows that DexVIP outperforms them on all evaluation metrics. Specifically, DexVIP slightly outperforms other methods on grasp success (% grasps that lifted the object for more than 50 timesteps), stability (% grasps robust to perturbations), and functionality (% grasps close to a ground-truth affordance region of the object). DexVIP achieves significant outperformance on posture (normalized distance of robot grasp pose to human grasp pose), a metric its additional reward term is designed to maximize.
- DexVIP achieves better performance with less training time (Fig 5, right) and less demonstration time (Fig 6) compared to the alternative methods. 
- The paper provides ablation experiments demonstrating the importance of the hand pose reward term, as well as experiments showing that DexVIP is robust to observation and actuation noise during training.
- The paper is well-structured and written clearly. 

Major Weaknesses:

**Scalability is limited by ContactDB data:**
The paper argues in lines 49-59, 65-66, and 303-326 that DexVIP scales better to new objects because of its use of video demonstrations for extracting hand pose priors (“New demonstrations are also easy to curate whenever new objects become of interest, since it is a matter of downloading additional video.”). However, the reward used for DexVIP still requires R_aff, the object affordance regions. If the object affordance maps require expensive human demonstrations with thermal cameras (on 3D-printed objects) for each object or object class, then this argument does not hold; the scalability would still be limited by the ability to collect this affordance data. The following additional experiments should be performed to measure the true scalability of DexVIP:

1) Train DexVIP without the affordance reward term and show the performance (this would show how scalable the method is based only on Youtube data)

2) Assuming that DexVIP makes use of the “Image affordance prediction model” from GRAFF (and this should be explicitly stated in the DexVIP paper), experiments should be added to show the performance of DexVIP on unseen object instances and unseen object classes for which ContactDB data has not been obtained. These experiments would show that additional thermal ContactDB data is not added for new instances or classes, and that the proposed method can rely on the generalization of the affordance prediction, as well as the scalability of Youtube videos. For the novel object instances, I believe that this can be achieved by training and evaluating only on the 11 non-ContactDB objects.This experiment is nearly done in the section “Expert data: teleoperation vs. YouTube video”, but that experiment only compares the performance of DexVIP and DAPG instead of comparing all methods). For novel object classes, an additional experiment should be added to show the scalability of DexVIP to novel classes for which ContactDB data is not available.

**Limited technical novelty:**
DexVIP has limited technical novelty, as it is mostly a combination of existing methods: GRAFF + FrankMocap.

**Minimal performance improvement:**
The results show only a small improvement over GRAFF, especially in grasping success, stability, and functionality, which are the most important metrics.  The ablation experiments show 
that adding the hand pose prior increases grasp success rate from 63 to 68%, a relatively small increase. There is a large improvement in “posture”, but this metric is the least important because it doesn’t really matter whether the robot performs a task in a human-like way, as long as the task is successfully completed. 



Minor Weaknesses
- Lines 271-272 state: “We take the policy trained on all 27 objects and first evaluate it on the 16 objects from ContactDB.” This seems like an odd procedure; it seems that a more straightforward evaluation would be to just train and evaluate on the 16 objects from ContactDB rather than training on 27 objects and only evaluating on a subset of them.
- Is the state space between the human and robot hands the same?  This isn’t discussed; if not, how do they bridge the morphological gap for computing the R_pose reward term? It is somewhat surprising to me that FrankMocap and the Adroit hand happen to use the same state space.
- DexVIP requires both hand pose priors extracted from video for its R_pose metric, as well as object affordance maps for the object affordances metric R_aff. For the 16 ContactDB training objects, the ground truth object affordance maps used in training are obtained from human demonstrations using thermal cameras. For non-ContactDB training objects, it is unclear from the paper how the object affordance maps are obtained. It is crucial to explain how these object affordance maps are obtained. 
- Fig. 6: The caption is not very descriptive compared to the other figure captions. 
- Supp. line 20: For the details in the supplement on the experiment with noisy observations and actions, is the 0.1 variance value in radians? This value does not seem to be used in [4] or [5] for similar noise-injected experiments. 
- Are the objects in the in-the-wild video demonstrations and the object meshes matched manually? This could be clarified in the paper.
- For the analysis in Appendix C, it would be interesting to see how often grasps that are similar according to standard taxonomies (https://www.is.mpg.de/uploads_file/attachment/attachment/256/grasp_taxonomy.pdf) are grouped together.

[1] Y. Rong, T. Shiratori, and H. Joo. Frankmocap: Fast monocular 3d hand and body motion capture by regression and integration. arXiv preprint arXiv:2008.08324, 2020.

[2] P. Mandikal and K. Grauman. Dexterous robotic grasping with object-centric visual affordances. In arXiv preprint arXiv:2009.01439, 2020.

[3] A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine. Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. Robotics: Science and Systems (RSS), 2018.

[4] Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kram r, R. Hadsell, N. de Freitas, et al. Reinforcement and imitation learning for diverse visuomotor skills. arXiv preprint arXiv:1802.09564, 2018. 

[5] OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al. Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 2020.

summary_of_recommendation: Extracting hand pose priors from in-the-wild video demonstrations seems promising for learning dexterous grasping with human-like hand poses. However, the current version of the approach seems to rely on other types of demonstrations such as thermal human demonstrations on 3D-printed objects for collecting ground truth object affordance maps, which weakens the claim made in the paper that DexVIP scales better for learning new objects. Further, the technical novelty is limited and the performance improvement compared to prior work is minimal.

# Post-rebuttal update

I would like to clarify that my concerns about the evaluation are not based on a misunderstanding.  The issue is that the 16 objects that were used in the main evaluation in the paper (Figure 5) were in the *training set* for the affordance prediction model.  Because these objects were in the training set, this evaluation does not demonstrate the generalization of the affordance prediction model; contactDB data was acquired and used for these specific objects that were used in the evaluation of Figure 5. The use of contactDB data significantly limits the scalability of the method, and it is unclear from Figure 5 whether the proposed method would still outperform the baselines on objects for which contactDB data is not available.  This is an issue because Figure 5 is the only figure in the main text in which DexVIP is compared to all of the baselines, and it is the only figure in the main text in which DexVIP is evaluated on a full range of metrics.

This issue is mostly rectified in Supp Sec. G.4 and Figure F. I would recommend to replace Figure 5 with Figure F in the final version of the paper. The downside of the approach used in Figure F, though, is that the functionality metric can no longer be included. Ideally, the paper should evaluate DexVIP on ContactDB objects that are in the affordance prediction model's test set (objects not used for affordance prediction training) so that the functionality metric can also be included, while still demonstrating generalizability to objects beyond those in the affordance prediction model's training set.

One small typo: the main text refers to "Supp Sec H", but the sections in the supplement only goes up to G.

Overall, I think the authors have significantly improved the paper during the rebuttal and addressed most of my concerns; with these updates, I think that the paper represents a solid contribution and thus I will update my recommendation to weak accept. 
issues: - Run an ablation removing the object affordance reward R_aff 
- Evaluate all methods only on the 11 objects not in ContactDB
- Evaluate all methods only on object classes not in ContactDB
- Clarify how object affordance maps are obtained for non-contact DB objects
- Clarify the difference between the state space between the human and robot hands
review_title: null

Meta-review: Reviewer Vrhm provides a good summary of the paper, "This work proposes an approach to learn dexterous robotic grasping from human-object interactions recorded in the video. Target hand poses are predicted from the video leveraging an existing method. The distances between target hand poses and current hand poses are used as a reward signal to train a deep reinforcement learning algorithm in the simulation. Besides the target hand poses, the affordance of each object is pre-calculated. The distances between the hand and affordance regions are also used as a reward signal. The proposed pipeline enables a 30-Dof hand to grasp 27 objects in the simulation achieving 68% success rates". 

Two reviewers have recommended the paper be accepted and one has recommended a rejection. Overall, reviewers like the idea of using youtube videos to improve grasping, which I agree is an interesting area of research.  But there are several issues in the proposed method. In particular, reviewer HJKw has well summarized them: 

- Run an ablation removing the object affordance reward R_aff
- Evaluate all methods only on the 11 objects not in ContactDB
- Evaluate all methods only on object classes not in ContactDB
- Clarify how object affordance maps are obtained for non-contact DB objects
- Clarify the difference between the state space between the human and robot hands

In my opinion, addressing these issues in the rebuttal is necessary. In addition, Vrhm raises concerns about "However, some important explanations of the proposed method are missing. It's unclear how the errors in the human pose prior affect the grasping performances. I would be happy to increase the evaluation score if these concerns are addressed.". Please address these concerns. 

Next, it would be good to point out on what object types is the difference between GRAFF and the proposed method the maximum. I agree with reviewers Vrhm and HJKw that the grasp success rate and not posture is the appropriate metric. 

Furthermore, reviewers have raised concerns about sim2real transfer. It would be worthwhile to conduct experiments with noisy observation and actuation to instill confidence that the method has a chance to work in the real world. Also, the details of physical parameters used in simulation requested by a reviewer are also critical. 

**Post Rebuttal Update**
The concerns of the reviewers have been addressed. All reviewers vote to accept the paper which I agree with. 
Decision: Accept (Poster)