Cross-Embodiment Dexterous Grasping with Reinforcement Learning
dexterous grasping;cross-embodiment learning;reinforcement learning
Yes
No ethics review needed.
- The authors used four dexterous hands for training and would like to include more different hands in the future. It would be interesting to investigate the impact of the number of hands used for training on the learning performance, e.g., generalizability. 
- The dexterous hands for training and testing consist of either 4-fingered or 5-fingered configurations with different DoFs. What would be the impact if for example one or more 3-fingered hand is also included for training?
- The authors randomized the origin position of the joint connecting the hand and the arm to increase robustness and transferability of the policy. What would be the impact of extending this to randomize additional hand parameters?
- The authors conducted a comprehensive benchmark and ablation study. 
- The presentation of the paper is clear and well-structured.
This paper learns a unified vision-based reinforcement learning policy via teacher-student learning to control various dexterous hands for grasping different objects. The authors propose a unified observation space across various hands, and a universal action space inspired by human hand teleoperation. The learned policy can generalize to unseen dexterous hands in a zero-shot manner.
- Only LEAP Hand was tested in the real-world experiments.
- Some failure cases should be included in the video.
- Joint position of the arm is included for training policies, which might affect the generalizability to different arms.
- More design choices should be analyzed regarding the impact on generalizability. See the questions below.
Yes
No ethics review needed.
For policy observation, how should different numbers of fingers be handled? From Figure 2, it seems the authors simply consider four fingers of the hand and ignore the others. If the paper only considers anthropomorphic hands with more than four fingers, this should be clearly stated.

Instead of predicting the eigengrasp and performing retargeting mapping, what is the performance of directly predicting fingertip positions?

When evaluating on unseen hands, does the retargeting mapping network use new hand data for training? If this is the case, I believe there should also be a comparison with baselines using those data.

Can you clarify how “MT-Raw-A” makes predictions on the unseen hands? From my understanding, it outputs joint positions for the unseen hands. But what if the unseen hand has a different number of joints compared to the training hands?

Minor questions and suggestions:
* The YCB dataset contains more than 70 objects. Why were only 45 YCB objects used? What was the criterion for selection?
This paper presents a clear story on training policies with multiple embodiments. The paper is well-written and illustrates the motivation from human teleoperation well.

The design of the action space (i.e., a linear combination of eigengrasp coefficients) is interesting.

The idea of using a neural network to approximate the retargeting is also interesting.

The authors also show that the policy can successfully transfer to the real world by training a student policy with vision as the input.
This paper presents a method for learning a unified policy for dexterous grasping that can transfer to unseen hand morphologies. It achieves this by first designing an observation space that contains only fingertip positions and palm pose, which can be shared between different hands. Then, the action space is based on the eigengrasp from the MANO hand model. By using the shared observation and action space, the authors can train a reinforcement learning policy on four different hands and transfer the learned policy to new hands.
From my understanding, the observation space contains four fingertip positions. It is not discussed how to deal with three or five fingers, or if the method is specifically designed for anthropomorphic hands. If this is an intrinsic assumption or limitation, it should be discussed in the main paper.

The authors design several ablation experiments to show the importance of the proposed observation and action space. However, there is still one design missing: What if we train a policy that directly predicts the fingertip positions and palm positions while keeping the observation space the same as the proposed method? The fingertip positions could then be converted to joint positions (independently for each finger) using inverse kinematics. This can also generalize to new hands as long as we have the kinematics structure of the new hand.

The retargeting mapping network needs training for each morphology, which means that when evaluating a new, unseen hand morphology, it requires extra data from the target hand. I’m concerned that this is not a fair comparison to the baselines, which does not use these data.

It is also concerning that increasing the number of eigengrasp does not significantly improve performance (Figure 4). This might indicate that the learned grasping actions are quite similar and may be limited for future applications such as functional grasping.

The real experiments are also limited; only three successful trials are demonstrated.
Yes
No ethics review needed.
- What exactly are the observations used by the visual policies, and how are they transferred to the real robot system? In lines 809-810 of the appendix, you mention that the vision policies are trained using DAgger in 16,384 parallel environments, which is double the number of environments used for state-based PPO training. Generating visual observations for such a large number of environments seems particularly challenging, especially given that prior works using student-teacher learning to distill vision policies in Isaac Gym have used two orders of magnitude fewer environments (https://arxiv.org/abs/2211.11744, https://arxiv.org/abs/2111.03043). You also state that "the object point cloud p ∈ R^(N×3), which contains N points captured by cameras." Is this captured by one camera per environment? If so, at what resolution, and how does the object point cloud differ from the point cloud obtained directly from the camera?
- Has a quantitative evaluation been conducted on real-robot deployment?
- In lines 346-347 it is stated that "The episode ends either when the task is completed or the object falls off the table". Are episodes also terminated after a certain number of time-steps?
- Is the object shape or ID represented to the state-based policies? If not, is the grouping of objects intended to reduce variability between them enough to allow learning a single grasping policy? If so, how is this grouping performed, and what are the characteristics of the resulting groups? A clearer explanation of the grouping process and its effectiveness in handling object diversity would help clarify how the policy generalizes across different objects.
- In Figure 3, the training performance of cross-embodiment learning is compared to individual training with the Shadow Hand. Is there a reason to specificallly use the Shadow Hand here, rather than the mean of the performance of all single-embodiment policies?
**Relevance of the Problem**
- The huge diversity of embodiments presents a significant challenge in embodied AI, making research on cross-embodiment learning highly relevant.

**Method**
- Leveraging pre-trained neural networks to approximate the optimization-based retargeting process across different embodiments is a intuitive approach to ensure efficient, parallelized training.

**Presentation**
- The writing is clear, and the logical flow of the paper effectively outlines studied problem and proposed solution, making it intuitive to follow.
- The visual presentation in Figures 1 and 2 is well-executed, providing a clear overview of the simulation setup and the input-output mapping from observations through policy actions to target joint positions.
This paper introduces CrossDex, a method for learning cross-embodiment dexterous grasping by defining a unified, embodiment-independent observation and action space. The action space utilizes eigengrasps of the human hand based on the MANO hand model. To enable efficient training, pre-trained neural networks replace the optimization-based retargeting process from eigengrasps to the joint angles of various embodiments. The policies are trained on grasping tasks using objects from the YCB dataset across different robot hands and evaluated for transferability to unseen embodiments. Additionally, visual policies operating on object point clouds are distilled using DAgger.
**Method and Evaluation**
- The baselines used in the evaluations are ablations of the proposed method and do not appear to be particularly strong for this task.
- Training on 4 embodiments and transferring to 2 novel ones is indeed quite limited, as acknowledged in the limitations section. However, attributing this limitation to 'restricted access to dexterous hand models' is not a compelling justification. Incorporating a simulation with a broader range of dexterous hand models and exploring how the performance of cross-embodiment policies scales with an increasing number of embodiments would add significantly value to the study.
- Details on how the vision policies are trained including camera resolution and a definition of the object point cloud are missing.


**Results and Claims**
- In section 5.3 it is stated that the cross-embodiment transferabiliy of the learned policy allows for direct deployment of the policy to new hands. This statement is not supported by the performance drop of over 50% between seen and unseen embodiments oberved for CrossDex in Table 1.
- In lines 254-255 you state that the "the dimensionality of human hand poses is significantly higher than the DoFs of most
dexterous hands, which can make RL less efficient." However, this high-dimensionality does not seem to be a challenge according to the results in Table 1, where policies using the joint-based action space outperform or perform simlarly to CrossDex in the PPO training stage. Further, related work has shown good performance with PPO in parallized simulators even with very high-dimensional action spaces (https://arxiv.org/abs/2305.12127).
- The comparison to baseline methods does not convincingly support the claim that using eigengrasps is an effective approach to solving this problem. Baseline policies based on joint-level control are not expected to transfer well across embodiments with different and unknown joint limits, especially when there is no information about the function of each joint. Using a baseline that directly controls keypoints in the MANO hand model, rather than using eigengrasps, would be a more appropriate baseline to validate this claim. This approach would more naturally align with the task of positioning the fingers into stable grasping configurations on an object’s surface. Moreover, Figure 9 in the Appendix highlights substantial differences in the resulting joint configurations for the same eigengrasp across different embodiments. For instance, in the first row of the figure, the second hand shows three fingers touching at the tips, whereas in the fifth hand, there is a significant gap between the fingers. These very different grasp configurations raise questions about the reliability of grasp transfer under such variation.
- In the ablation on 'Eigengrasp Actions,' Figure 4 examines the effect of varying the number of eigengrasps on performance it is inferred that the method is relatively insensitive to this change. However, the finding that comparable performance can be achieved with just a single eigengrasp raises important questions. This outcome may reflect a characteristic of the problem being studied, which might not require the nuanced control offered by a multi-fingered hand, rather than demonstrating a strength of the proposed method itself.


**Minor Points on Writing**
- In line 483, "affect" should be "affects".


***

Overall, the fact that baselines not specifically designed for cross-embodiment transfer still achieve non-negligible success raises doubts about the suitability of the proposed approach. Moreover, a success rate below 40% on unseen embodiments does not justify the claim of direct transferability. This calls into question the overall feasibility of the framing. Additionally, the observation that a single eigengrasp performs comparably well on the task suggests that the task itself may not be sufficiently challenging. More complex manipulation problems are needed to meaningfully assess the impact of the proposed action space.
@inproceedings{
anonymous2024crossembodiment,
title={Cross-Embodiment Dexterous Grasping with Reinforcement Learning},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=twIPSx9qHn},
note={under review}
}
Dexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, the development of a universal policy that controls diverse dexterous hands remains largely unexplored.
In this work, we study the learning of cross-embodiment dexterous grasping policies using reinforcement learning (RL). Inspired by the capability of human hands to control various dexterous hands through teleoperation, we propose a universal action space based on the human hand's eigengrasps. The policy outputs eigengrasp actions that are then converted into specific joint actions for each robot hand through a retargeting mapping. We simplify the robot hand's proprioception to include only the positions of fingertips and the palm, offering a unified observation space across different robot hands. Our approach demonstrates an 80% success rate in grasping objects from the YCB dataset across four distinct embodiments using a single vision-based policy. Additionally, our policy exhibits zero-shot generalization to two previously unseen embodiments and significant improvement in efficient finetuning. For further details and videos, visit our project page.
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dexterous grasping cross-embodiment learning reinforcement learning
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applications to robotics, autonomy, planning
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Cross-Embodiment Dexterous Grasping with Reinforcement Learning
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