Original: A N E MPIRICAL S TUDY AND A NALYSIS OF L EARNING G ENERALIZABLE M ANIPULATION S KILL IN THE SAPIEN S IMULATOR
Revised: A N E MPIRICAL S TUDY AND A NALYSIS OF L EARNING G ENERALIZABLE M ANIPULATION S KILL IN THE SAPIEN S IMULATOR

Original: Anonymous authors

Original: Paper under double-blind review

Original: A
Revised: A

Original: This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021.
Revised: This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021.

Original: Our approach follows an end-toend pipeline which mainly consists of two steps:first, we  extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network.
Revised: Our approach follows an end-toend pipeline which mainly consists of two steps: we first extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators through a deep and wide transformer-based network.

Original: More specially, to open up avenues for exploitation of learning manipulation skill, we present an empirical study that includes a bag of tricks and abortive attempts.
Revised: More specially, to open up avenues for exploitation of learning manipulation skill, we present an empirical study that includes a bag of tricks and abortive attempts.

Original: Finally, our method achieves a promising ranking on the leaderboard.
Revised: Finally, our method achieves a promising ranking on the leaderboard.

Original: All code of our solution is available at https://github.com/******.
Revised: All code of our solution is available at https://github.com/liu666666/bigfish codes.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: We perceive the real world by seeing the 3D environments and manipulating 3D objects surrounding us.
Revised: We perceive the real world by seeing the 3D environments and manipulating 3D objects surrounding us.

Original: Nowadays, researchers in the computer vision and deep learning community pay more and more attention to establishing artificial intelligence that can recognize and interact with objects in 3D scenes as human beings do.
Revised: Nowadays, researchers in the computer vision and deep learning community pay more and more attention to establishing artificial intelligence that can recognize and interact with objects in 3D scenes as human beings do.

Original: To this end, SAPIEN Open-Source Manipulation Skill Challenge, termed as ManiSkill Challenge Mu et al.
Revised: To this end, SAPIEN Open-Source Manipulation Skill Challenge, termed as ManiSkill Challenge Mu et al.

Original: (2021), was introduced to concentrate on learning unseen objects manipulation skills.
Revised: (2021) was introduced to concentrate on learning unseen objects manipulation skills.

Original: Based on the SAPIEN Manipulation Skill Benchmark (ManiSkill Benchmark)
Revised: Based on the SAPIEN Manipulation Skill Benchmark (ManiSkill Benchmark)

Original: Xiang et al.
Revised: Xiang et al.

Original: ManiSkill challenge utilizes a variety of articulated objects in a full-physics simulator to accomplish 4 tasks: Opening Cabinet Door, Opening Cabinet Drawer, Pushing Chair, and Moving Bucket.
Revised: ManiSkill challenge utilizes a variety of articulated objects in a full-physics simulator to accomplish 4 tasks: Opening Cabinet Door, Opening Cabinet Drawer, Pushing Chair and Moving Bucket.

Original: Figure 1 demonstrates four tasks according to the examples from ManiSkill Benchmark, which consists of 162 objects and 36,000 successful demonstrations in total.
Revised: Figure 1 demonstrates four tasks according to the examples from ManiSkill Benchmark which consists of 162 objects and 36,000 successful demonstrations in total.

Original: It is noted that these tasks require manipulating the unseen test objects after learning on training objects where the training and testing samples are from the same class.
Revised: It is noted that these tasks require manipulating the unseen test objects after learning on training objects where the training and testing samples are from the same class.

Original: To learn the generalizable manipulation skill, we propose an end-to-end framework that extracts the 3D visual features and predicts the action of the robot arms.
Revised: To learn the generalizable manipulation skill, we propose an end-to-end framework that extracts the 3D visual features and predicts the action of the robot arms.

Original: More specially, we adopt the PointNet
Revised: More specially, we adopt the PointNet

Original: Qi et al.
Revised: Qi et al.

Original: (2017) to learn the point cloud features and then utilize a transformer Vaswani et al.
Revised: (2017) to learn the point cloud features and then utilize a transformer Vaswani et al.

Original: (2017) based network to output the action of robots.
Revised: (2017) based network to output the action of robots.

Original: Moreover, we provide an empirical study that is mainly composed of useful tricks and failure trials to learn generalizable manipulation skills.
Revised: Moreover, we provide an empirical study that is mainly composed of useful tricks and failure trials to learn generalizable manipulation skills.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: To clarify, we classified the related works into two aspects: 3D simulators and point cloud feature extractors.
Revised: To clarify, we classified the related works into two aspects: 3D simulators and point cloud feature extractors.

Original: At present, more and more 3D simulators are constructed for Embodied AI.
Revised: At present, more and more 3D simulators are constructed for Embodied AI.

Original: AI2-THOR Kolve et al.
Revised: AI2-THOR Kolve et al.

Original: (2017) is a platform that provides the manipulation capabilities of agents, especially the interaction with actionable objects in four room categories: bed, bathroom, kitchen, and living room.
Revised: (2017) is a platform that provides the manipulation capabilities of agents, especially the interaction with actionable objects in four room categories: bed, bathroom, kitchen, and living room.

Original: The
Revised: The

Original: Interactive Gibson Environment (iGibson) Xia et al.
Revised: Interactive Gibson Environment (iGibson) Xia et al.

Original: (2020) can render dynamical environments by using photogrammetry for their room construction.
Revised: (2020) can render dynamical environments by using photogrammetry for their room construction.

Original: Besides, iGibson provides users with ten fully functional and common robotic agents, which can improve the application in the real world.
Revised: Besides, iGibson provides users with ten fully functional and common robotic agents, which can improve the application in the real world.

Original: This challenge adopts the SAPIEN environment that supplies the interaction with diverse articulated objects without any commercial software.
Revised: This challenge adopts the SAPIEN environment that supplies the interaction with diverse articulated objects without any commercial software.

Original: More details about 3D simulators can be found in this survey Duan et al.
Revised: More details about 3D simulators can be found in this survey Duan et al.

Original: This challenge simulates a moving panoramic camera that provides ego-centric point clouds or
Revised: This challenge simulates a moving panoramic camera that provides ego-centric point clouds or

Original: RGB-D images.
Revised: RGB-D images.

Original: Following Mu et al.
Revised: Following Mu et al.

Original: (2021), we adopt the point clouds as the 3D visual input and utilize the PointNet Qi et al.
Revised: (2021), we adopt the point clouds as the 3D visual input and utilize the PointNet Qi et al.

Original: (2017) as the feature extractor.
Revised: (2017) as the feature extractor.

Original: PointNet extracts the powerful point cloud feature by learning pointwise features independently through several MLP layers and modeling global features via a max-pooling layer.
Revised: PointNet extracts the powerful point cloud feature by learning pointwise features independently through several MLP layers and modeling global features via a max-pooling layer.

Original: PointNet ++ Charles et al.
Revised: PointNet ++ Charles et al.

Original: (2017) improves PointNet by designing a hierarchical network to model fine geometric architecture from the neighborhood of each point.
Revised: (2017) improves PointNet by designing a hierarchical network to model fine geometric architecture from the neighborhood of each point.

Original: Furthermore, Structural Relational Network (SRN) was proposed in Duan et al.
Revised: Furthermore, Structural Relational Network (SRN) was proposed in Duan et al.

Original: (2019) to learn structural relational information between different local units using MLP.
Revised: (2019) to learn structural relational information between different local units using MLP.

Original: More details about point cloud can be referred to this paper Guo et al.
Revised: More details about point cloud can be referred to this paper Guo et al.

Original:  M ETHOD
Revised:  M ETHOD

Original: In this section, we introduce our method briefly.
Revised: In this section, we introduce our method briefly.

Original: Basically, we adopt an end-to-end architecture to extract the point cloud features and predict the action of robot arms.
Revised: Basically, we adopt an end-to-end architecture to extract the point cloud features and predict the action of robot arms.

Original: Specifically, we utilize the
Revised: Specifically, we utilize the

Original: PointNet to extract the 3D feature of manipulated objects and develop a deep and wide transformerbased network to output the direction and the moving distance of the arm simulators.
Revised: PointNet to extract the 3D feature of manipulated objects and develop a deep and wide transformerbased network to output the direction and the moving distance of the arm simulators.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: To give guidance for future work, we present an empirical study that includes a bag of tricks and abortive attempts.
Revised: To give guidance for future work, we present an empirical study that includes a bag of tricks and abortive attempts.

Original: Specially, we tried various combinations of training samples with different trajectories, different head numbers in transformer architecture, the depth of transformer network, batch size, iteration number, and so on.
Revised: Specially, we tried various combinations of training samples with different trajectories, different head numbers in transformer architecture, the depth of transformer network, batch size, iteration number, and so on.

Original: We report the results of four tasks on Table 1, Table 2, Table 3 and Table 4, respectively.
Revised: We report the results of four tasks on Table 1, Table 2, Table 3 and Table 4, respectively.

Original: We can observe that the opening cabinet drawer and opening cabinet door is easier than moving a bucket and pushing the chair.
Revised: We can observe that the opening cabinet drawer and opening cabinet door is easier than moving a bucket and pushing the chair.

Original: Specially, the former tasks achieve more than 0.4 success rate while the latter tasks obtain less than 0.3 success rate.
Revised: Specially, the former tasks achieve more than 0.4 success rate while the latter tasks obtain less than 0.3 success rate.

Original: To our surprise, models that perform close on the training set can differ by more than 10% points on the testing set, such as row 3 and row 4 in Table 1.
Revised: To our surprise, models that perform close on the training set can differ by more than 10% points on the testing set, such as row 3 and row 4 in Table 1.

Original: Besides,
Revised: Besides,

Original: [Table] [Table]
Revised: [Table] [Table]

Original: as shown in row 4 and row 5 in Table
Revised: as shown in row 4 and row 5 in Table

Original: [Table]
Revised: [Table]

Original: submitting the same model can achieve a 1.2% increment.
Revised: submitting the same model can achieve a 1.2% increment.

Original: Next [Table] we summary up some advice from two aspects: useful tricks and failed attempts.
Revised: Next, we summary up some advice from two aspects: useful tricks and failed attempts.

Original: In this section
Revised: In this section

Original: [Table]
Revised: [Table]

Original: we list a bag of useful tricks.
Revised: we list a bag of useful tricks.

Original: Compared with baseline Mu et al.
Revised: Compared with baseline Mu et al.

Original: (2021), the model trained with a larger batch size and longer iteration number can bring significant performance improvement.
Revised: (2021), the model trained with a larger batch size and longer iteration number can bring significant performance improvement.

Original: Take row 1 and row 2 in Table 1 as an example, larger batch size and longer iteration number results in 9.1% absolute increment from 24.5% to 33.6% on the Opening Cabinet Drawer task.
Revised: Taking rowand row 2 in Table 1 as an example, larger batch size and longer iteration number results in 9.1% absolute increment from 24.5% to 33.6% on the Opening Cabinet Drawer task.

Original: Consequently, we adopt these hyper-parameters settings for the following experiments.
Revised: Consequently, we adopt these hyper-parameters settings for the following experiments.

Original: Compared with the model trained with suitable iteration and batch size, to some degree, deeper (more blocks) and wider (more heads) transformer networks increase the success rate drastically.
Revised: Compared with the model trained with suitable iteration and batch size, to some degree, deeper (more blocks) and wider (more heads) transformer networks increase the success rate drastically.

Original: Take row 2 and row 3 in Table 1 as an example, wider networks, e
Revised: Taking row 2 and row 3 in Table 1 as an example, wider networks, e

Original: [Table]
Revised: [Table]

Original: g., from 4 heads to 64 heads, bring 6.8% improvement in terms of testing success rate.
Revised: g., from 4 heads to 64 heads, bring 6.8% improvement in terms of testing success rate.

Original: As for deeper networks, Take row 3 and row 4 in Table 1 as an example, 12 blocks outperformblocks significantly ( 10.4% ) on the testing set.
Revised: As for deeper networks, Taking row 3 and row 4 in Table 1 as an example, 12 blocks outperform 6 blocks significantly ( 10.4% ) on the testing set.

Original: [Table]
Revised: [Table]

Original: On the contrary
Revised: On the contrary

Original: [Table]
Revised: [Table]

Original: we present some useless efforts.
Revised: we present some useless efforts.

Original: We construct different training sets by generating samples using various trajectory lengths, such as 100 trajectories, 200 trajectories, and 300 trajectories.
Revised: We construct different training sets by generating samples using various trajectory lengths, such as 100 trajectories, 200 trajectories, and 300 trajectories.

Original: Then we combine them as several training sets to learn skills.
Revised: Then we combine them as several training sets to learn skills.

Original: As shown in row 4 of Table 2, we did not get any increment.
Revised: As shown in row 4 of Table 2, we achieve improvement on the training set but not on testing set.

Original: We also tried some different transformer variants, such as Bhojanapalli et al.
Revised: We also tried some different transformer variants, such as Bhojanapalli et al.

Original: Regrettably, as shown in row 6 of Table 4, no improvement is achieved.
Revised: Regrettably, as shown in row 6 of Table 4, no improvement is achieved.

Original: We believe that approaches that perform poorly on the training set will 
Revised: We believe that approaches that perform poorly on the training set will also not achieve good results on the testing set.

Original: also

Original: not achieve good results

Original: on the testing set.

Original: Hence

Original:  we did not submit some solutions to the challenge since they do not get promising results on the training set.
Revised: Hence, we did not submit some solutions to the challenge since they do not get promising results on the training set.

Original: This kind of solution includes replacing 64 heads with  heads, replacing 12 blocks with 18 blocks, and different learning rates.
Revised: This kind of solution includes replacing 64 heads with 256 heads, replacing 12 blocks with 18 blocks, and different learning rates.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: Table 5 shows the final result and ranking of all participating teams.
Revised: Table 5 shows the final result and ranking of all participating teams.

Original: Finally, we got a top-3 ranking in this no interaction track of the ManiSkill challenge.
Revised: Finally, we got a top-3 ranking in this no interaction track of the ManiSkill challenge.

Original: More importantly, to promote the research on manipulating unseen objects in the SAPIEN simulator, we release our models and scripts on Github as a new baseline.
Revised: More importantly, to promote the research on manipulating unseen objects in the SAPIEN simulator, we release our models and scripts on Github as a new baseline.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this technical report, we present a brief description of our solution to the no interaction track of SAPIEN ManiSkill Challenge 2021.
Revised: In this technical report, we present a brief description of our solution to the no interaction track of SAPIEN ManiSkill Challenge 2021.

Original: Our method utilizes an end-to-end pipeline which mainly consists of two steps: first, we extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators via a transformer-based network.
Revised: Our method utilizes an end-to-end pipeline which mainly consists of two steps: first, we extract the point cloud features of multiple objects; then we adopt these features to predict the action score of the robot simulators via a transformer-based network.

Original: More specially, to give guidance for future work, we provide an empirical study that includes a bag of tricks and some useless attempts.
Revised: More specially, to give guidance for future work, we provide an empirical study that includes a bag of tricks and some useless attempts.

Original: Finally, our method achieves a top ranking on the leaderboard.
Revised: Finally, our method achieves a top ranking on the leaderboard.

Original: In future works, we will further explore to manipulate unseen objects through designing suitable learning from demonstrations (LfD) algorithms.
Revised: In future works, we will further explore to manipulate unseen objects through designing suitable learning from demonstrations (LfD) algorithms.