Venue: CoRL 2023 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_of_paper: This work proposes a method for learning to grasp and execute functional grasping motions on objects with a dexterous hand by using a combination of learning from demonstrations and reinforcement learning. Their key contributions are a) using eigengrasps to reduce the action space of the hand from demonstrations and reduce the difficulty of exploration for reinforcement learning based methods, b) a sim to real approach that leverages a hierarchical policy which breaks the task into a pre-grasp, grasp, and post-grasp trajectory. Their method experimentally validates their contribution by demonstrating their method's superior success rate and performance in simulated and realistic robotic grasping and control environments.
review: Overall, the clarity of this paper is very well presented, and clearly delivers an original approach to improving dexterous grasping using a combination of human demonstration and reinforcement learning, which they demonstrate on real hardware. 

This paper shows a fairly well-developed set of comparisons across competing metods, including policies implemented as a VAE, feed-forward, and without the eigengrasp action space. It would be worth showing in the supplemental how different combinations of these other methods, for instance using the VAE with the unconstrained actions, could potentially improve the performance of either, since the VAE would implicitly learn a compressed representation of the actions, without relying on the eigengrasps as yet another lower-dimensional representation of the hand poses. 

The experimental setup is also clearly described and demonstrated, although it is also very surprising that the tele-op "oracle" performs worse than the trained policy on lifting some of the objects, and it is certainly a strong positive result from this proposed method.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: I recommend this paper be accepted given it's strong demonstrated results on real hardware. It also showcases a novel use of eigengrasps as a way of simplifying the action space for dexterous grasping and manipulation. However, this work would benefit from clearly contextual comparisons to related work, which go largely unaddressed. Lastly, this paper includes a fairly convincing set of demonstrations, with results that seem to outperform the human demonstrator on specific objects, which should be built on further in future work in terms of scaling the approach.
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 addresses the functional grasping of objects with a dexterous hand. The process of dexterously manipulating an object is broken down into 3 stages including approaching the object with a pre-grasp that enables subsequent manipulation, stably picking up the object, and finally executing a post-grasp trajectory for tool use. This paper mainly investigates the second stage where a RL policy is learned to pick up objects in simulation and then transferred to the real world.
review: Strengths:
1. Several design choices are made to improve the performance of the grasping policy including using a recurrent neural network to encode the history of joint angles during the grasping motion and transforming the action space from the raw joint angles to linear weights for 9 eigengrasps computed from mocap data. These two strategies are shown to improve the success rate in simulation. 
2. The problem this paper studies is relevant. Transitioning task-oriented grasping and tool use from parallel jaw grippers to dexterous hands is a logical and necessary step.

Weakness:
1. The paper claims to achieve functional grasping for objects in the wild. However, the diversity of the objects is rather limited. Most of the functional grasps seem to be on objects with large cylindrical handles. It would be necessary to show whether the proposed method can generalize to objects with more complex shapes such as scissors, cups, glasses, and knives.
2. The main algorithmic novelty seems to be limited to the use of the eigengrasps. 
3. It’s unclear whether other vision-based policies can outperform the blind policy and whether visual observation is needed to generalize to more diverse objects. Comparisons with vision-based methods are needed to verify the benefit of the blind policy.
4. Even though this paper presents a complete pipeline including determining pre-grasp, picking up the object, and finally executing the manipulation trajectory. The first and last part of the pipeline is rather limited and lack experiment validation. There are many details that need to be considered to build a robust pipeline. For example, how to map pre-grasp poses to objects in random 6-DoF poses and how to recognize the poses of the object in hand after the grasping motion such that the post-grasp trajectory can be reliably executed.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: This paper mainly introduces a method to stably pick up objects with large handles with a dexterous hand. The policy is trained in RL and has shown reasonable transfer to the real world. This grasping module can potentially be combined with task-oriented pre-grasp and post-grasp adjustment to achieve dexterous tool use. However, only the grasping module is evaluated and the whole pipeline is left to be validated.
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 paper proposes a system for functional grasping of household objects with a multifingered robot hand. The objects is placed without clutter on a tabletop and its image is matched to a canonical image of an object of the same class. Functional grasp affordance analysis on that image object is thus transferred to the real object, and the robot and is moved to the vicinity of the grasp location. Next, an RL policy trained in simulation is used to pick up and grasp the object. Finally, the arm is moved away using a keyframed motion.

Experiments are conducted on a real robot setup and the proposed algorithm is compared to a teleoperation policy that is recorded using cybergloves, and a hardcoded grasping primitive.
review: ### Strengths
- The paper clearly formulates the research problem, describes a method to solve it, and evaluates it with experiments. The paper is well written and easy to understand.
- Strong real robot performance.
- Teleoperation as a baseline is a nice idea, because it also upper bounds the imitation learning baseline.

### Weaknesses
- The paper does not evaluate the accuracy of the affordance prediction method it uses.
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I am voting to "weak accept" this paper because of strong experimental performance on a real robotic system for a relevant and non-trivial problem - functional grasping with multi-fingered hand. The paper clearly mentions the research problem and shows that the proposed method addresses it.

### Post-rebuttal
The authors addressed both my concerns, so I am upgrading my rating to "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: The presented work deals with the problem of generating functional grasps for arbitrary objects  with dexterous robot hands. The paper proposes a two-phase method in which first an affordance point in the image is found and the robot is moved to a pre-grasp pose. Then, a learned RL policy is applied to properly grasp the object. To learn the RL policy, the authors propose a sim2real approach.

To obtain the pre-grasp pose in the image, the authors leverage DINO-ViT features. The authors propose matching the features of a previously annotated object with the object of interest. This feature matching will provide a (x,y) pixel in the image that is the one that best matches the features of the annotated image. 

Once in the pregrasp pose, the grasping policy is learned by RL in a reduced dimensional action space that is linear to the original action space (Eigengrasps).
review: The paper is well written and it is easy to follow. The authors motivate their problem properly with an example that helps the reader understand the complexity of the problem to be tackled. 

One limitation of their introduction is the lack of proper introduction of previous approaches to the same problem. It is important to frame the proposed method in comparison with other works. This allows to frame the papers contribution in contrast with previous approaches. Is there any work aiming to solve dexterous grasping? How do they tackle their problem? What are their limitations? How does your proposed method improve over those limitations?

While the paper is not introducing novel methods in the algorithmic side, the proposed model might be interesting from a systems perspective. Nevertheless, the author fail to cite previous works on similar approaches in which the robot skill is represented as a two-phase problem of choosing a place to go and executing the skills. These methods are usually known as Coars-to-Fine approaches [1,2].

The paper presents a good evaluation of the grasp policy and compare their proposed EigenGrasp with other approaches that reduces the action space. Nevertheless, there is no any evaluation of the pre-grasp selection phase. Given, the pre-grasp selection is claimed as a contribution, it is mandatory to have an evaluation of that part.


**Strenghts**

1. The proposed problem is challenging and the proposed solution is interesting and makes sense.

2. The real-robot experiments for such a challenging task are highly appreciated.

**Weaknesses**

1. The lack of visual/tactile input in the RL policy limits the scope of the solution. The robot might need a visual signal to properly execute the grasping.

2. While the authors provide an interesting comparison of different approaches in the RL part, the paper is composed of two distinguished parts. The authos should provide a proper comparison of the affordance based approach with other affordance based methods, such as CLIP-Port [3] or Transported-Networks [4]. 

3. The paper is missing proper citations to Coarse-to-fine works that frame the problem in similar way.


[1] Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration, Johns

[2] Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning, Valassakis et al.

[3] Shridhar, Mohit, Lucas Manuelli, and Dieter Fox. "Cliport: What and where pathways for robotic manipulation." Conference on Robot Learning. PMLR, 2022.

[4] zeng, Andy, et al. "Transporter networks: Rearranging the visual world for robotic manipulation." Conference on Robot Learning. PMLR, 2021
robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I suggest a rejection in the current stage of the work. Nevertheless, I believe that the paper could be an accepted paper if they properly address the current weaknesses.
review_title: null

Meta-review: The authors present a method to perform functional grasping of objects with a dexterous hand. They do this by breaking down the problem into multiple components. They perform affordance prediction from image observations, followed by a scripted move to a pre-grasp pose. They then use an RL policy in simulation to pick up and manipulate the object and transfer this to the real world. The authors present experiments on a real world system and break down evaluation of both the pre-grasp performance (added during rebuttal) and the post-grasp RL system. The experiments and ablations are quite comprehensive.

Reviewers highlighted that the rebuttal responses were compelling and this AC agrees that the rebuttal is particularly strong. Early feedback was primarily around experimental setup and missing ablations. In fact this was consistent with all reviewers. The authors addressed this feedback almost entirely by providing new experimental results and comparing (both experimentally and via updated manuscript content) with existing works..

While this did not impact the final recommendation, one AC comment would be to refrain from highlighting and summarizing reviewer comments that are not necessarily consistent with all reviewer feedback. As an example, “novel approach” is highlighted yet this is misleading as not all reviewers considered the system components novel, yet the author's response would suggest it is.
Decision: Accept (Poster)