Paper: Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models (ICLR-2024)

Reviewer comment (Evaluation):
I think the analysis of the results of the real-world experiment is a little insufficient. In the results shown in Table 2, RT-2-X performs much better on scene C, so why does it perform better only on scene C? RT-2-X is said to have been learned through larger model and more data, but that does not show such performance in scene A and B, but analysis on this part seems to be insufficient.

Author response:
> **RT-2-X performs much better on scene C, so why does it perform better only on scene C?**

After the submission deadline, we made some improvements to the subgoal diffusion model as we discuss in Appendix A.4. We re-evaluated in all scenes and found that our method now outperforms RT-2-X, even in Scene C. You can find the updated results at [this link](https://ibb.co/hKDHFFB), as well as in Table 2 in the updated PDF.

Regarding why RT-2-X performs badly in Scene B, this is because each scene is challenging in different aspects. **Semantically**, Scene C is the most challenging since the robot needs to carefully ground the language command to identify the unseen objects and requires a high generalization capability. However, each object is in a shape that is relatively easy to grasp. In contrast, Scene B is challenging in the sense of manipulating the plastic bell pepper --- this object requires particular precision to grasp due to being light, slippery, and almost as wide as the gripper. We have updated the PDF to clarify this point. 

We found that while RT-2-X is good at semantic generalization for unseen objects, it lacks precision in picking up challenging objects like the bell pepper. In contrast, as we discussed in Section 5.4, the subgoal generated by SuSIE improves the precision of low-level control for manipulating these challenging objects.

Reviewer comment (Evaluation):
In order to generate subgoal well, an overall temporal understanding of the task is required. But there is not much clear analysis or intuition why the fine-tuned image-editing model performs better than the video prediction model.

Author response:
> **There is not much clear analysis or intuition why the fine-tuned image-editing model performs better than the video prediction model.**

Modeling an entire video puts a very high burden on the generative model, requiring the frames to obey strict physical consistency. Unfortunately, we find that current video models often produce temporally inconsistent frames (hallucinations), which only confuse the low-level controller, inhibiting it from completing the task. After the submission deadline, we evaluated the video model baseline (UniPi) on the real robot but we found that it performed poorly due to the hallucinations. We show the updated table at [this link](https://ibb.co/hKDHFFB). Qualitative examples of UniPi rollouts can be found at [this link](https://ibb.co/MpBtjSJ). While it’s definitely possible that better video models in the future can reach better performance, we found no open-source video model that could get good results despite considerable effort (no code for the original UniPi work was ever released, though we tried to follow a reimplementation used by one of its authors for [another paper](https://arxiv.org/abs/2309.08587)).

Reviewer comment (Evaluation):
The results from the simulated environment (CALVIN) don’t show much significant performance difference from other language-conditioned policy baselines.

Author response:
> **The results from the simulated environment(CALVIN) don’t show much significant performance difference.**

Perhaps there is some misunderstanding here. The simulation results of SuSIE in the CALVIN benchmark (0.75, 0.46, 0.19, 0.11, 0.07) improve over the next best baseline AugLC, which scores (0.69, 0.43, 0.22, 0.09, 0.05), by a fair margin. 

Furthermore, after the submission deadline, we realized that we used sub-optimal values of classifier-free guidance parameters for the SuSIE’s evaluations on CALVIN. After simply tuning these values, it led to further performance gains posted in the latest draft of the paper **(0.87, 0.69, 0.49, 0.38, 0.26)**. Kindly refer to these numbers as the measured performance of SuSIE. The updated table can be seen at [this link](https://ibb.co/bJnJcPL).

Reviewer comment (Experiments):
It would be better if the authors could provide additional experiments with the baselines which utilize low-dimensional image embeddings as subgoals rather than high-dimensional raw images to show that image-editing model is effective as a subgoal generator.

Author response:
> **It would be better if the authors could provide additional experiments with the baselines which utilize low-dimensional image embeddings as subgoals.**

We are willing to add this baseline, but we are not sure which method this is referring to. Do you have any specific methods or papers in mind that you would suggest we include?

Reviewer comment (Experiments):
When learning the goal-reaching policy, the model is learned to provide subgoals within k steps of any state. But I wonder if how much the change in k value makes a difference in performance. It would be nice to see additional experiments showing changes in performance according to the value of k.

Author response:
> **I wonder if how much the change in k value makes a difference in performance.**

While a full ablation of this hyperparameter would be ideal, retraining the diffusion model and re-evaluating the policy many times is prohibitively expensive. As such, we chose $k$ by inspecting each dataset and choosing a value that will be about one-third of the average length of the trajectories in that dataset. For example, in the case of BridgeData, since most of the trajectories are around 20-40 steps, we set $k$ to 10. In the case of the CALVIN dataset, as most of the demos are 65 steps, we set $k$ to 20. Though simple, we found this choice to generally lead to good results. We have added this clarification in Appendix A.6 in the updated PDF.

Reviewer comment (Writing):
Some parts could be reformulated/reworded better in Section 4 to reduce ambiguities and improve reproducibility.

Author response:
**Some parts could be reformulated/reworded better in Section 4**

Thank you for pointing this out. We have now reformulated the notation in Section 4.1 in the updated PDF.

Reviewer comment (Experiments):
The information regarding the datasets used can be provided more comprehensively and at an earlier stage in the paper.

Author response:
**The information regarding the datasets used can be provided more comprehensively and at an earlier stage in the paper.**

We wanted to avoid excessively repeating information that is covered in a prior BridgeData V2 paper (Walke et. al., 2023), but have now added additional information about datasets in Appendix A.7. We are happy to move these details to the main paper for the final version, where there would be an extra page available.

Reviewer comment (Reproducibility):
The figures depict properly selected sub-goals for the scenes, such as a pre-grasp pose in Figure 1. However, it is unclear if or how the sub-goal synthesis procedure is optimized to output such intuitive sub-goals, rather than less important/effective transitionary scenes that simply happen to follow the input pose. In essence, how do you select/optimize for the feasible/relevant subgoals?

Author response:
**In essence, how do you select/optimize for the feasible/relevant subgoals?**

Our model generates feasible subgoals because it is trained on feasible subgoals: the training data consists of predicting an intermediate frame in a real trajectory that corresponds to the desired task. We do not do anything special to pick out “important” subgoals, so it is quite possible the model will produce “less important transitionary scenes” – that is a very valid point. However, we empirically observe that the model learns to generate useful subgoals from this training alone and the generated subgoals are sufficient to improve the performance of the downstream low-level controller, as evidenced by the good overall performance of our method in the real-world evaluations. We chose $k$, the horizon for the subgoals, by inspecting each dataset and choosing a value that will be about one-third of the average length of the trajectories in that dataset. For example, in the case of BridgeData, since most of the trajectories are around 20-40 steps, we set $k$ to 10. In the case of the CALVIN dataset, as most of the demos are 65 steps, we set $k$ to 20. Though simple, we found this choice to generally lead to good results. We do agree that a smarter way of adaptively choosing subgoals could lead to even better results. We have added this clarification in the updated paper.

Reviewer comment (Evaluation):
Table 1 uses some success rates from another paper (Ge et al., 2023). However, the approach proposed in this cited paper achieves higher success rates (see 'Ours' row in Table 2 of Ge et al.) than the ones reported in this paper. Why did you omit this result? Ge et al. seem to also address out-of-distribution generalization. Other than the 1-step chain case (theirs 72% vs. SuSIE 75%), the longer chains achieve better results than SuSIE.

Author response:
**Table 1 uses some success rates from another paper (Ge et al., 2023). However, the approach proposed in this cited paper achieves higher success rates**

We believe that the reviewer is referring to the non-zero-shot evaluation from Ge et al. 2023, which assumes access to interaction data from environment D (the target environment). All of the other methods we report, including ours, are performing zero-shot transfer, using the training environments (A, B, C) and without ever training on any trials from environment D. We believe it is reasonable to include only the zero-shot evaluations, and we’ve clarified this in the paper.

That said, our updated CALVIN numbers in the revised draft (0.87, 0.69, 0.49, 0.38, 0.26) also substantially outperform this omitted result from Ge et al., 2023 (0.72, 0.47, 0.30, 0.13, 0.11). After the submission deadline, we realized that we used sub-optimal  values of classifier-free guidance parameters for the SuSIE evaluations on CALVIN. Simply tuning this value led to the currently reported result. The updated table can be seen at [this link](https://ibb.co/bJnJcPL).

Reviewer comment (Evaluation):
It was mentioned that Scene C should be a more difficult task than Scene B, however the results in Section 5.3 show that both MOO, RT-2-X and SuSIE perform significantly better on Scene C than on B. Can you explain what the reason might be? Is it related to the VLM or the low-level control policy?

Author response:
**It was mentioned that Scene C should be a more difficult task than Scene B, however the results in 5.3 show that both MOO, RT-2-X and SuSIE perform significantly better on scene C than on B. Can you explain what the reason might be?**

We apologize for the confusion. *Semantically*, Scene C is the most challenging since the robot needs to carefully ground the language command to identify the unseen objects and requires a high generalization capability. However, each object is in a shape that is relatively easy to grasp. In contrast, Scene B is challenging in the sense of manipulating the plastic bell pepper — this object requires particular precision to grasp due to being light, slippery, and almost as wide as the gripper. As such, because of these differences, it is unclear if absolute performances of different methods should be compared across these scenes. We have updated Section 5.1 in the new PDF to clarify this point.

Reviewer comment (Experiments):
The model is illustrated to depict sub-goals nicely as it is able to distinguish between particular objects to orient the gripper towards. But there are non-visual properties of these objects that low-level controllers need to consider to perform pick and place tasks, such as weight and friction. This model however seems to rely on visual properties only. Would the robot in this pipeline be able to pick up such challenging objects, such as a heavy and slippery object, even after a few tries?

Author response:
**Would the robot in this pipeline be able to pick up such challenging objects, such as a heavy and slippery object, even after a few tries?**

This is a good question. As discussed in the previous question, the bell pepper in Scene B is a challenging object to pick up – being light and slippery. We found that the subgoal generated by SuSIE improves the precision of low-level control for manipulating the challenging objects, as we discussed in Section 5.4. We also show qualitative videos on the [website](https://subgoal-image-editing.github.io/) under “Enhanced Precision -> Comparison Videos.”

Reviewer comment (Evaluation):
InstructPix2Pix is not capable of performing viewpoint changes. How would this challenge be reflected in a real-life or research scenario where there are often multiple or moving cameras present?

Author response:
**InstructPix2Pix is not capable of performing viewpoint changes, how would this challenge be reflected in a real-life or research scenario where there are often multiple or moving cameras present?**

While solving tasks involving changes in viewpoint is beyond our current focus, we believe this challenge can be addressed by training on more video data (e.g., Ego4D or Open X-Embodiment) that includes multiple or moving cameras. Another potential solution is to condition InstructPix2Pix on both historical and current observations. However, this is outside the scope of our work. We’ve added a discussion of this limitation of our evaluation setup to Section 6.

Reviewer comment (Experiments):
Often, the gripper may be obscured in the camera view due to other objects, such as the rest of the robot. The first phase may output these subgoals as a suggestion of pre-grasp or grasp pose. However, due to decoupling, the low-level controller would have no way of knowing this is the case. In the image of the scene, any pose of the gripper would look the same. How would the scene play out—would the robot be able to recover or get stuck?

Author response:
**The gripper may be obscured in the camera view due to other objects. How would the scene play out, would the robot be able to recover or get stuck?**

In our robot setup, the gripper will never be completely obscured by obstacles. As discussed in the previous question, while this is beyond our focus, a potential solution in such partially observable tasks is to condition InstructPix2Pix on both historical and current observations, or add multiple cameras to the setup.

Reviewer comment (Experiments):
Can you elaborate on the role/effect of the conditional policy (Section 4.2)? It seems to enforce the robot to follow the trajectory sampled in the dataset when it encounters a similar state and subgoal. What happens when it encounters, for example, a similar current state but a different subgoal (that was not in the dataset) during test time?

Author response:
**What happens when it encounters, say, a similar current state but a different subgoal (that was not in the dataset) during test time?**

We believe that our evaluation in Scene B already follows a similar setup to answer your question – this scene has a seen tabletop, a seen object (bell pepper), and a combination of a seen container (orange pot) and an unseen container (ceramic bowl). This means that while the initial state is similar to the state in the dataset, for the task “put the bell pepper in the ceramic bowl,” our method must produce a correct out-of-distribution subgoal. Our experiments demonstrate that our diffusion model can not only generate reasonable subgoals, but the low-level policy can also effectively handle these out-of-distribution subgoals.

Reviewer comment (Experiments):
Does your robot dataset consist of only completely successful trajectories? If not, could a subgoal be generated for failure (e.g., robot holds the desired object in the current state, but subgoal generates a state where it is dropped)? If so, how do you handle such cases?

Author response:
**Does your robot dataset consist of only completely successful trajectories?**

Yes, our datasets consist of successful trajectories. It will be an interesting direction to utilize negative trajectories and failure cases, which we leave to future work.

Reviewer comment (Presentation):
There is a minor error in the table references. In Section 5.3 and A2.2.2, you mentioned "Table 5.3," which refers to "Table 1."

Author response:
**There is a minor error in the table references. In Section 5.3 and A2.2.2, you mentioned "Table 5.3," which refers to "Table 1."**

Thanks for the catch! We have fixed the reference in our updated PDF.

Reviewer comment (Experiments):
**W1.1**: Is the pre-training improving the downstream policy performance? The paper only presents a qualitative comparison of predicted sub-goals. Authors should report quantitative results, controlling the low-level policy to reach subgoals generated by the 2 models (with and without InstructPix2Pix pre-training) to show that the Internet-scale pre-training brings a boost in performance.

Author response:
This is a good question! We have conducted an additional real robot experiment evaluating the from-scratch model in Scene C. As shown in the table, the from-scratch model is completely unable to perform the task, which demonstrates that the InstructPix2Pix initialization is essential for robust generalization. The videos of the from-scratch model can be found at [this link](https://ibb.co/XXrxFbt), including the generated subgoals in the top row. More additional qualitative examples can be found at [this link](https://ibb.co/4fR6NQk) showing that in unseen environments, the model without InstructPix2Pix initialization (the column “from scratch”) produces noticeably worse images.

| Task | From Scratch | Ours |
| -------- | -------- | -------- |
| Toothpaste in bowl | 0.0     | 0.5     |
| Crayon in bowl      | 0.0     | 0.6     |
| Spoon in bowl      | 0.0     | 0.4     |
| Bowl to top      | 0.0     | 0.3     |

Reviewer comment (Experiments):
**W1.2**: Are the predicted subgoals really physically plausible, and is it a pre-requisite for the low-level policy to succeed in controlling the robotic arm? By looking at Figure 3, it is not easy to verify that sub-goals are physically plausible. Further experiments should be conducted to verify this quantitatively. Moreover, authors should conduct experiments to show that having physically plausible sub-goals improves the performance of the low-level policy. A possible scenario might be that sub-goals are not always physically plausible, but through its training, the low-level policy has learned to come as close as possible to sub-goals while avoiding ending up in any undesirable state.

Author response:
We apologize for the confusion. While we do not explicitly evaluate the plausibility of a given subgoal, we found that the predicted intermediate subgoals tend to provide more fine-grained feedback on gripper orientations and pose at the intermediate steps of a trajectory, which allows the policy to be more precise. The previous comparison with the from-scratch model empirically supports this claim –  while the from-scratch model generates implausible subgoals and is useless for performing the task, the subgoals generated by SuSIE are sufficiently plausible for effective low-level control.

In addition, to further investigate if having plausible sub-goals improves the performance, we conducted a comprehensive comparison with Oracle GCBC (GCBC with the ground-truth final goal). SuSIE constantly achieves higher success rates even compared to executing privileged ground-truth final goals as shown in [this table](https://ibb.co/x8gDN7M). We also show qualitative videos on  [our website](https://subgoal-image-editing.github.io/) under “Enhanced Precision -> Comparison Videos.” 

We are happy to further revise the wording in the paper if that'd be helpful to improve clarity.

Reviewer comment (Evaluation):
**W2**: **[Major]** How are findings from Figure 4 generalizable? It shows qualitative rollouts from the proposed method and RT-2-X which seem to indicate that RT-2-X manipulates objects with less precision. However, quantitative results in Table 2 show that RT-2-X outperforms the introduced method in certain tasks, showing it might be more precise in some cases.

Author response:
After the submission deadline, we made some improvements to the subgoal diffusion model as we discuss in Appendix A.4. We re-evaluated in all scenes and found that our method now outperforms RT-2-X, even in Scene C. You can find the updated results at [this link](https://ibb.co/hKDHFFB), as well as in Table 2 in the updated PDF.

In addition, to further support our results presented in Figure 4, we conducted a comprehensive comparison with Oracle GCBC as we showed in the previous question. These results demonstrate that the findings from Figure 4 are generalizable.

Reviewer comment (Experiments):
**W3**: **[Major]** RT-2-X outperforms the method on certain tasks. However, as mentioned by the authors, RT-2-X was trained on a large robotics dataset and the proposed method still showcases promising performance on most tasks. I thus believe this smaller performance compared with RT-2-X on scene C does not outweigh the interesting contributions of this work.

Author response:
After the submission deadline, we made some improvements to the subgoal diffusion model as we discuss in Appendix A.4. We re-evaluated in all scenes and found that our method now outperforms RT-2-X, even in Scene C. You can find the updated results at [this link](https://ibb.co/hKDHFFB), as well as in Table 2 in the updated PDF.

In addition, to further support our results presented in Figure 4, we conducted a comprehensive comparison with Oracle GCBC as we showed in the previous question. These results demonstrate that the findings from Figure 4 are generalizable.

Reviewer comment (Reproducibility):
**W4**: **[Minor]** To the best of my knowledge, the policy action space is not described in the main paper. A description should be added.

Author response:
The action space is seven-dimensional, consisting of a 6D relative Cartesian motion of the end-effector, and a discrete dimension to control the gripper action. We have added the description in Section 4.3 in the updated PDF.

Reviewer comment (Reproducibility):
**W5**: **[Minor]** Even if authors properly reference the paper of interest and refer to the appendix later, the following sentence is quite vague: “To obtain a robust goal-reaching policy in Phase (ii), we follow the implementation details prescribed by Walke et al. (2023).” Additional details should be added directly in the main paper following this sentence.

Author response:
Due to the page limit, we describe the details of the goal-reaching policy in Appendix A.1.1. We will include the description in the main paper for our final version (which will have an extra page for).

Reviewer comment (Evaluation):
There is no discussion on how to handle the situation if the generated subgoal is incorrect, nor are there any mechanisms for error detection and recover.

Author response:
Although there is no explicit error detection, we find that the replanning mechanism at test-time execution (Algorithm 1) makes SuSIE surprisingly adept at recovering from failures – as our algorithm regenerates a new subgoal every K steps, even if an incorrect subgoal is accidentally generated, it can still successfully achieve the task as long as it generates the next subgoal correctly. You can find some qualitative examples of failure recovery at [this link](https://ibb.co/b61yQ4F), as well as more on [our website](https://subgoal-image-editing.github.io/) under “Enhanced Precision -> Comparison Videos.” We have added this discussion to the paper.

Reviewer comment (Novelty):
Most of the tasks presented in the paper appear to be easily solvable using pre-trained perception modules and traditional planning methods.

Author response:
We evaluated a total of **9 tasks across three different scenes**, and we believe that the difficulty and number of tasks in our evaluation are sufficient to answer the 4 research questions we pose at the beginning of Section 5. It is worth noting that we have adopted a common evaluation scope, as seen in other recent works on data-driven robot learning, that were also published at top ML and robotics conferences such as ICLR, ICML, CoRL, and RSS [1,2,3,4,5,6,7]. 

[1] studies only simulation tasks and did not include any real-world evaluations. [2] only evaluated 1 single tabletop scene for 4 tasks on the real robot. [3] also studies only 1 single scene for 9 tasks, and all tasks are picking and placing. [4] considers 3 scenes, which consist of 2 seen scenes in the training dataset. [5] studies 3 scenes, but all scenes look similar and have less variability than ours. [6] and [7] study a similar number of scenes as ours, consisting largely of picking and placing on a tabletop.

[1] Yu et al., ​​Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks, ICLR 2023.

[2] Ma et al., VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training, ICLR 2023.

[3] Ma et al., LIV: Language-Image Representations and Rewards for Robotic Control, ICML 2023.

[4] Kumar et al., Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials, RSS 2023.

[5] Myers et al., Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control, CoRL 2023.

[6] Walke et al., BridgeData V2: A Dataset for Robot Learning at Scale, CoRL 2023.

[7] Stone et al., Open-World Object Manipulation using Pre-Trained Vision-Language Models, CoRL 2023.

Reviewer comment (Experiments):
Real-world evaluation is conducted in just three scenes, which may not fully capture the complexity and variability of actual manipulation tasks.

Author response:
We evaluated a total of **9 tasks across three different scenes**, and we believe that the difficulty and number of tasks in our evaluation are sufficient to answer the 4 research questions we pose at the beginning of Section 5. It is worth noting that we have adopted a common evaluation scope, as seen in other recent works on data-driven robot learning, that were also published at top ML and robotics conferences such as ICLR, ICML, CoRL, and RSS [1,2,3,4,5,6,7]. 

[1] studies only simulation tasks and did not include any real-world evaluations. [2] only evaluated 1 single tabletop scene for 4 tasks on the real robot. [3] also studies only 1 single scene for 9 tasks, and all tasks are picking and placing. [4] considers 3 scenes, which consist of 2 seen scenes in the training dataset. [5] studies 3 scenes, but all scenes look similar and have less variability than ours. [6] and [7] study a similar number of scenes as ours, consisting largely of picking and placing on a tabletop.

[1] Yu et al., ​​Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks, ICLR 2023.

[2] Ma et al., VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training, ICLR 2023.

[3] Ma et al., LIV: Language-Image Representations and Rewards for Robotic Control, ICML 2023.

[4] Kumar et al., Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials, RSS 2023.

[5] Myers et al., Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control, CoRL 2023.

[6] Walke et al., BridgeData V2: A Dataset for Robot Learning at Scale, CoRL 2023.

[7] Stone et al., Open-World Object Manipulation using Pre-Trained Vision-Language Models, CoRL 2023.

Reviewer comment (Experiments):
In Algorithm 1, I was wondering how to determine the key frames that cannot be described in a single instruction. You mentioned sampling a new subgoal every K steps. How do you choose the goal sampling interval K to ensure the subgoals are plausible enough?

Author response:
While a full ablation of this hyperparameter would be ideal, retraining the diffusion model and re-evaluating the policy many times is prohibitively expensive. As such, we chose $k$ by inspecting each dataset and choosing a value that will be about one-third of the average length of the trajectories in that dataset. For example, in the case of BridgeData, since most of the trajectories are around 20-40 steps, we set $k$ to 10. In the case of the CALVIN dataset, as most of the demos are 65 steps, we set $k$ to 20. Though simple, we found this choice to generally lead to good results. We do agree that a smarter way of adaptively choosing subgoals could lead to even better results.

Reviewer comment (Theory):
In 5.2 you mention "an understanding of dynamics to predict how to move and rotate the gripper to grasp it." How can you determine the plausibility of the prediction in terms of dynamics, given that the generating subgoals and learning low-level control policies are separate?

Author response:
We apologize for the confusion. We have revised this sentence in the updated PDF. While we do not explicitly evaluate the plausibility in terms of the dynamics of a given subgoal, we found that the predicted intermediate subgoals tend to provide more fine-grained feedback on gripper orientations and pose at the intermediate steps of a trajectory, which allows the policy to be more precise, and this is what we meant by this sentence.

In addition, we found that pre-training is crucial for our model to generate fine-grained subgoals for the low-level policy to handle. We conducted an additional experiment to compare SuSIE to the model without InstructPix2Pix initialization (trained from scratch on robot data). As shown in the table, the from-scratch model generates implausible subgoals and is completely unable to perform the task. This demonstrates the fact that the subgoals generated by SuSIE are sufficiently plausible for the low-level control. The qualitative videos of the from-scratch model can be found at [this link](https://ibb.co/XXrxFbt), including the generated subgoals in the top row.

| Task | From Scratch | Ours |
| -------- | -------- | -------- |
| Toothpaste in bowl | 0.0     | 0.5     |
| Crayon in bowl      | 0.0     | 0.6     |
| Spoon in bowl      | 0.0     | 0.4     |
| Bowl to top      | 0.0     | 0.3     |

In short, while explicit detection of subgoal plausibility will likely improve the performance further, our experimental results show that our current system, although simple, is already capable of effectively performing the tasks. We are happy to further revise this wording if that'd be helpful to improve clarity.

Reviewer comment (Evaluation):
It seems that an extremely accurate pose of the end effectors is needed. I am somewhat concerned that the precision of the genarated subgoal frame may not be sufficient to achieve it.

Author response:
As mentioned above, whether or not the subgoals are precise enough is an empirical question that we answer with our experiments. Our comparison with Oracle GCBC (Section 5.4) demonstrates that SuSIE consistently achieves higher success rates even compared to executing privileged ground-truth final goals. In addition, we further compared SuSIE with Oracle GCBC in all scenes and the results can be found at [this link](https://ibb.co/x8gDN7M). Qualitative examples are provided in Figure 4 as well as on [our website](https://subgoal-image-editing.github.io/) under “Enhanced Precision -> Comparison Videos.” These results demonstrate that the subgoals generated by SuSIE are sufficiently accurate to improve the precision of low-level control. Generally, we do not believe that it is necessary to have particularly accurate poses for the end effector, nor did we observe this to be a significant factor – perhaps this stems from some misunderstanding of the method? Could you clarify why you believe that the end effector poses need to be highly precise? If this is due to confusing writing on our part we would like to correct it!