ACE: A Cross-platform and visual-Exoskeletons System for Low-Cost Dexterous Teleoperation

Authors: shiqi  yang
minghuan  liu
yuzhe  qin
runyu  ding
jialong  li
xuxin  cheng
ruihan  yang
sha  yi
xiaolong  wang
Venue: robot-learning_org_CoRL_2024_Conference
Published: 2024-05-14 09:08:34

Abstract: Bimanual robotic manipulation with dexterous hands has a large potential workability and a wide workspace as it follows the most natural human workflow.
Learning from human demonstrations has proven highly effective for learning a dexterous manipulation policy. To collect such data, teleoperation serves as a straightforward and efficient way to do so.
However, a cost-effective and easy-to-use teleoperation system is lacking for anthropomorphic robot hands.
To fill the deficiency, we developed \our, a cross-platform visual-exoskeleton system for low-cost dexterous teleoperation. 
Our system employs a hand-facing camera to capture 3D hand poses and an exoskeleton mounted on a base that can be easily carried on users' backs. ACE captures both the hand root end-effector and hand pose in real-time and enables cross-platform operations. 
We evaluate the key system parameters compared with previous teleoperation systems and show clear advantages of \our.
We then showcase the desktop and mobile versions of our system on six different robot platforms (including humanoid-hands, arm-hands, arm-gripper, and quadruped-gripper systems), and demonstrate the effectiveness of learning three difficult real-world tasks through the collected demonstration on two of them.

Main paper: 
 introduction: 
 In recent years, the effectiveness of training robot foundation models using real-world robot data has led to a significant increase in attention to data collection. Teleoperation has emerged as a crucial data collection method, enabling researchers to demonstrate and record complex robotic tasks. Various solutions, including VR systems [1, 2, 3] , motion capture systems [4, 5] , wearable gloves [6, 7] , and low-cost devices [8, 9] , have been developed to facilitate teleoperation, each offering unique advantages in terms of accessibility, precision, and generalizability. As we assess these existing systems, a critical question arises: What key information must be captured to effectively perform dexterous manipulation tasks on a wide range of robot platforms?The answer is quite simple: accurate hand poses and end-effector positions, which can be achieved with low-cost hardware. We propose ACE, a cross-platform visual-exoskeletons system for low-cost dexterous teleoperation, as illustrated in Figure 1 . For hand pose capture, we utilize a low-cost camera combined with a 3D hand pose estimation method [10, 11, 12] . To address the common issue of occlusion on vision-based systems, our design features a camera mounted on the end effector of the exoskeleton, ensuring it always follows the front face of the hand. The exoskeletons, which are 3D printed, provide precise end-effector positions through forward kinematics. With this, the robot hardware does not need to match the morphology of the teleoperation system. During teleoperation, as the operator moves the exoskeletons and their hands, we capture the hand root end-effector position and hand pose in real-time. We then use inverse kinematics to map the robot arm end-effector with the human hand end-effector position. This allows for effective motion retargeting between human hands and robot hands, enabling precise control of the robot's fingers. ALOHA [8] Parallel-Jaw 20k Joint-matching Mobile-ALOHA [13] Parallel-Jaw 32k Joint-matching ✓ GELLO [9] Parallel-Jaw 0.6k Joint-matching AnyTeleop [14] Anthropomorphic ∼ 0.3k Vision We compare our teleoperation system with several recently proposed systems, as shown in Table 1 , to illustrate our advancement. The ALOHA [8, 13] system provides precise control of bimanual manipulators, primarily focusing on parallel-jaw grippers. By directly matching the joints between the teleoperation system and the robot, ALOHA can achieve precise control of the end-effector. However, this advantage limits its usage to only the specific designated hardware. In contrast, our system allows the operation of multiple robot hardware types and offers the flexibility to control multi-finger robot hands instead of only parallel-jaw grippers. This flexibility enables data collection on more diverse tasks. Our system can even be adapted to 2-DOF grippers using simplified hand gestures. The GELLO [9] system achieves one-to-one joint matching by using a smaller-sized robot arm to control the actual robot arm. However, we show in our experiments that this mismatch in size makes it hard to control for fine manipulation tasks. Our system, on the contrary, provides more precise control by focusing on transferring the motion of only the end-effectors. AnyTeleop [14] uses cameras to capture hand poses and perform teleoperation directly with the camera data. While this method pushes the boundary of low cost and simplicity in teleoperation hardware, the visionbased system can only provide limited accuracy in measuring the hand root end-effector poses.Our system, which employs an exoskeleton for capturing hand poses, provides a more accurate and reliable measurement of the hand root end-effector.Our system leverages the precision of kinematics-based exoskeletons, combined with the affordability and adaptability of vision-based systems. Acting as a bridge between these two approaches, we can obtain accurate hand poses and end-effector positions at a low cost while ensuring crossplatform compatibility. In this paper, we introduce our teleoperation system, including the hardware design and the software modules that enable cross-platform operations. Our experiments show two key advantages. First, our designed system enables users to quickly adapt and efficiently, accurately, and swiftly complete tasks under various precision and workspace requirements. Second, our imitation learning experiments demonstrated that our system can efficiently collect effective data and complete the corresponding imitation learning. 
related work: 
 Learning from human demonstrations. Learning from human demonstrations has emerged as a widely adopted approach in robotic manipulation, leveraging the ability to replicate complex tasks by observing human actions. In-the-wild data from human hand movements offers a rich and accessible source of demonstration data for robot learning [2, 16, 17, 18, 19, 20] . Recent studies have shown that utilizing teleoperation for robot control and data collection is both efficient and transferable [13, 14, 9, 21, 22] . However, collecting large-scale datasets in real-world environments is often time-consuming and resource-intensive [23, 24, 25, 26] . To enhance efficiency and expedite data collection, it is crucial to develop systems that are generalizable to a wide range of robot platforms, end-effectors, and tasks.Vision-based Teleoperation. Teleoperation for manipulation has a long history [27] , with common solutions like VR [2, 1, 28, 29, 30, 31] , motion capture devices [4, 5] , and gloves [6, 7] , smartphones [32, 33] , and other devices [34, 35] . Those systems provide accurate tracking poses for manipulation tasks. However, high-cost devices are less accessible, which limits large-scale data collection. In contrast, vision-based teleoperation is particularly adaptable and user-friendly [36, 37, 38] , which requires less hardware setup, can be adapted to different body types, and is relatively lowcost without complex hardware setups. Effective hand-tracking algorithms [10, 11, 12] allow for the transfer of human hand motions to anthropomorphic robot hands [39, 14, 40, 38] . Direct wrist tracking and motion transfer allow for cross-platform operations on a wider range of robot platforms [3, 18, 39, 41, 42] . However, vision-based tracking systems are sensitive to occlusion, which introduces ambiguities in the hand poses. Wrist pose estimation in the world coordinate can also be inaccurate and noisy.Kinematics-based Data Collection. Data collection directly from the robot itself has also been employed [43] . Recent works of developing a replica of a robot have shown high efficiency for data collection [44] and teleoperation [8, 9] . These systems mirror the target robot's kinematic structure, enabling accurate movement and interaction replication. The controlling process is straightforward with joint-matching [13, 9, 45] . Exoskeletons are also intuitive solutions for human operators, but normally with a significantly higher cost [46, 47, 48, 49] and do not support hand modeling. Our work is also related to recently proposed DexCap [15] (Table 1 ) which utilizes a hand motion capture system with a mobile base, with more expensive hardware. Its wrist tracking is based on real-time localization, which is more accurate than pure vision methods but introduces delay. Compared to DexCap, our system allows cross-platform generalization with enhanced accuracy and flexibility using a low-cost design. 
system design: 
 To develop a cross-platform and low-cost dexterous teleoperation system that enables efficient large-scale data collection, we summarize the desiderata into the following five principles: Crossplatform Compatibility: Ensuring versatility across various robot platforms, such as fixed-based robot arms, quadrupeds, and humanoids, while accommodating different end-effector types, including anthropomorphic hands and parallel-jaw grippers. Accuracy: Transferring the precise hand and wrist pose of the operator to actual robot platforms. This enables fine-grained manipulation tasks and improves the success rate of data collection. Low-cost: Emphasizing affordability to facilitate easy prototyping and lower the barrier to large-scale data collection. This encourages the use of readily available, off-the-shelf components and materials, instead of expensive components like VR headsets or motion tracking systems. User-friendly: Ensuring the system is intuitive to use, requiring minimal expertise for calibration and operation, and accommodating users of different heights and weights. We also aim for two types of bases -fixed or mobile -so the user can easily switch between fixed and mobile tasks. Easy to Manufacture and Maintain: Designing the system to be straightforward to manufacture and maintain, with easily replaceable components to minimize downtime and extend operational lifespan. This includes a modular design using standard components to support easy assembly, disassembly, and replacement.An overview of our ACE system design is illustrated in Fig. 2 . Our system consists of a 3D-printed bimanual exoskeleton and two hand-facing low-cost webcams mounted at the ends of the exoskeleton. The exoskeleton's joint position tracking provides accurate wrist pose by forward kinematics, and the two cameras provide precise hand-pose tracking. By combining these two sources of information, the system enables real-time hand pose tracking relative to the world coordinate.After obtaining the accurate hand pose, we map it to target robot poses on various robot platforms.After the control mapping, we can teleoperate robots and collect demonstrations for imitation learning. In experiments, we demonstrate the effectiveness of our teleoperation system on a range of robots with various end-effectors, including the Xarm with Ability Hand, H1 with Inspire Hand, B1 with Z1, Franka with a gripper, and GR-1 with a gripper.Dual-base setup. The proposed ACE system, shown in Fig. 3 , has two arms and two types of bases: desktop and mobile. This dual-base setup provides cross-platform compatibility from mobile robots to robot arms. The desktop version provides a stable and precise wrist pose from its stationary base. For long-term teleoperation, the user may rest their elbows on the platform. The mobile base is designed for tasks that need constant movement or continuous adjustment of the field of view. Specifically suitable for mobile platforms including humanoids and quadrupeds.Servos and cameras. Our system features two arms, each with seven links, six degrees of freedom (DoF), a wrist, and a camera mount. Each arm is equipped with a UCB2Dynamixel (U2D2) controller and DYNAMIXEL XL330-M288-T servos with high-resolution 12-bit encoders for accurate joint position readings, ensuring precise end-effector tracking. The two-DoF camera mount allows for optimal hand positioning to avoid occlusion issues common in vision-based hand tracking. Magnetic connections. We utilize a modular design with magnetic connections, as illustrated in Fig. 3 . The use of magnets is not only inexpensive but also significantly enhances the user experience. Traditionally, donning exoskeleton equipment can be a complex process. However, our magnetically modular design allows users to independently don the system (the desktop version) in less than 30 seconds. Additionally, the magnetic connections facilitate quick and easy size adjustments. The wrist size can be altered almost instantly, and the upper and lower arm length adjustments can be completed in under 2 minutes. This modular design allows easy integration of updated components, enabling efficient upgrades to our open-source system.When the user starts collecting demonstrations, we estimate the precise wrist pose and hand keypoints. The wrist pose is determined by reading joint angles from the exoskeleton's encoders and calculating the pose using the forward kinematics of the designed exoskeletons [50] . Hand images are processed using MediaPipe [11] , a lightweight, RGB-based hand detection tool that can operate in real-time on a CPU, to detect the 21 hand keypoints in the wrist frame.After obtaining the wrist and hand pose data, we map human movements to target robot poses and end-effector control signals across various platforms. To meet the specific needs of different platforms and tasks, we address three significant challenges in developing the control interface: workspace mismatch, control scale variability, and usability concerns.Matching workspace sizes. First, the variation in workspace sizes poses a significant challenge on different platforms, particularly in achieving full coverage during bimanual operations. For a given platform, users can specify the required workspace in the configuration. By wearing our device and moving their arms around, we align the center of the human and robot workspaces and document the matching scale between these two workspaces.Control scale variability. Second, different tasks and platforms require varying levels of control precision. For example, when using a scaled replica of the actual robot platform, direct jointmatching teleoperations will result in errors being amplified when transferring the motion to a larger robot arm [9] . Moving the small teleoperation end-effector by a small distance may translate to much larger movement on the actual robot arm, significantly impacting control accuracy for fine tasks. Instead, in our system, by using inverse kinematics to map the end-effector positions, we can provide a more accurate and intuitive mapping from the teleoperation device to the actual robot.Consider the 6D robot end-effector pose x e ∈ R 6 , and the human wrist pose x h ∈ R 6 , we map x h to x e as x e = γ(x h -c h ) + c t (1) where γ is the control scale, c h is the center of the human workspace, and c t is the center of the task workspace. More detailed parameter definitions are included in the Appendix A. We show that in our experiments, this scaled approach is more efficient and adaptable to different workspaces and task sizes than direct joint matching methods. Usability. The biggest challenge for cross-platform teleoperation is usability. Cross-platform deployment often involves redundant and labor-intensive calibration processes when switching platforms. To enhance usability and accommodate different tasks and robot platforms, we have different control modes as an additional layer before transferring the end-effector pose to the actual robot:• Normal Mode: Direct transfer end-effector positions to the actual robot. The end-effector pose in this mode x normal e is the same as in Eq. ( 1), where γ is the ratio between the robot and human workspace radius.• Mirror Mode: Designed for large robots, this mode allows users to operate face-to-face with the robot. In this mode x mirror e = -γ mirror e (x h -c h )+c t where the end-effector motion is mirrored, thus making it more intuitive during operation.• Bimanual Mode: For bimanual tasks that require high precision, the cameras may collide. We adjust the scaling factor γ and c t so that the left and right hand side workspaces align. In this setting, when the camera mounts are about to collide, the robot hands touch each other.Based on different types of end-effectors, we also support:• Gripper Mode: Tailored for parallel jaw grippers, this mode provides straightforward control for tasks suited to this end-effector type. The distance between the thumb tip and the index fingertip is linearly mapped to a range of 0 to 1.• Hand Mode: The hand and fingertip motions are retargeted to the robot hand; detailed principles are provided in the Appendix B.After mapping the wrist poses to the robot's coordinate frame, the joint angles of the robot arm can be obtained using inverse kinematics (IK). Different robot platforms may require specific filters, PID parameters, and constraints to ensure safety, smooth movements, and reduce issues from singularity. The robot hand joints are obtained from human hand poses and mapped through hand motion retargeting [14] . We have optimized the computational efficiency to ensure a hand pose tracking frequency of approximately 27 Hz, which can be further improved to 100 Hz with a higherfrequency camera. These approaches ensure that our teleoperation system is adaptable, precise, and  
experiments: 
 We designed experiments to investigate two key aspects of our proposed teleoperation system: precision and transferability. We designed a target-reaching task to show that our system can achieve precise end-effector control, compared to previous joint-matching systems. We show how robots autonomously perform tasks via imitation learning trained using the data collected from our system, and we show more teleoperation examples in Fig. 4 .Precision evaluation. Similar to other joint matching methods [51, 45, 9] , the end-effector pose obtained from the joint encoder is highly accurate. Specifically, in our system, the average forward kinematics error is around 1 mm. As shown in Fig. 5 , we perform teleoperation on a measurement tape extension task. By extending the tape from 20 cm to 40 cm, we show that the teleoperation end-effector accurately represents the operator's motion, with an average error of only 3mm. The errors presented in this figure are cumulative, including both teleoperation and robot control errors, yet the overall error remains within a reasonable range.Target-reaching evaluation. We compare the agility and precision of our ACE system with the previous joint-matching system, specifically GELLO [9] for its comparable cost and open-sourced implementation. We designed a target-reaching task to evaluate the performance of both systems in a simulation. The operator's objective was to control the end-effectors to reach randomly generated target regions. Success was recorded when both end-effectors reached their respective target area and remained within the target regions for a short duration.We set up four scenarios to assess teleoperation performance, with workspaces and target regions of small, medium, and large sizes. Our evaluation involved six operators. Further details about the task configurations and operator specifics are provided in Appendix C.3. We evaluate the performance of the system by the time used to reach the target region, the average reaching speed, the average end-effector moving speed, and the effective ratio, which is the ratio between the optimal moving distance and the actual moving distance of the end-effector.The result is shown in Table 2 . ACE system consistently outperformed GELLO across most scenarios and metrics, demonstrating shorter time costs, higher reaching speeds, higher effective ratios, and success rates. Because of the combination of using Forward Kinematics (FK) for end-effector tracking, and Inverse Kinematics (IK), allows for adaptable mapping from the operator pose to the robot pose. Although the direct joint-matching method in GELLO provides accurate control, it is only suitable for a specific workspace and task size (medium size as defined in our experiment). For smaller workspaces, or fine tasks that require precise motion, the amplified robot motion in GELLO has a hard time stabilizing to the given position, resulting in redundant or sub-optimal motion. Our system ACE utilizes an adaptable approach that first maps the operator's natural workspace to the task's required workspace, and also adjusts the control scale on the robot platform to fit the required tasks and workspaces. This adaptable approach is only available when we can transfer the scaled poses with inverse kinematics to solve the robot configurations. This shows that our approach is not only more generalizable to different robot platforms and end effectors but also adaptable to tasks and workspaces of different scales.We design six real-world tasks that require multi-step dexterous teleoperation, illustrated in Fig. 6 . Two long horizon tasks, Vacuum Keyboard: 1) grasp the vacuum, 2) activate the vacuum, 3) clean the keyboard, 4) return the vacuum; Serve Candies: 1) grasp the candy tube, 2) open the cap, 3) throw the cap into the trash bin, 4) pour candies into the box, and 5) throw the tube into the trash bin. Wipe Whiteboard: grasps the eraser and wipes the whiteboard. Grasp Dolls: grasps the dolls one-by-one and put into the basket. Put in Tennis: grasp a tennis ball and put it into the container.For Grasp Dolls, it requires generalizations to different objects and robot positions. In particular, we trained on nine types of small dolls and two types of large dolls but tested on 15 types of small dolls and three types of large dolls. We collected data with ACE system on xArm with Ability Hand and H1 with Inspire Hand on the above-mentioned tasks. Tab. 3 shows that with ACE, we can efficiently collect a large number of data with a high success rate.With the data collected from teleoperation, we train each policy with imitation learning. We use 3D diffusion policies [52] for imitation learning on xArm platforms and ACT [8] on H1. Specifically, for diffusion policy, we draw point clouds independently from two camera views, and the proprioception states as the robot observation and the action space is the target poses of the arm wrists, and hands. The input for ACT is a single RGB image from a camera mounted at the top of the robot. The learning results for all tasks are shown in Tab. 3. We tested 10 episodes for each task and showed the success rate on different tasks. 
conclusion: 
 In this paper, we proposed ACE: a cross-platform visual-exoskeleton system for low-cost dexterous teleoperation. ACE system employs a hand-facing camera to capture 3D hand poses and an exoskeleton mounted on either a fixed or a mobile base. This setup accurately captures both the hand root end-effector and hand pose in real-time, enabling cross-platform operations with low-cost hardware.Our system does present two major limitations. The camera mount prevents the wearer's hands from coming together and, additionally, increases the burden of rotational movements due to the added moment arm. Although functionally addressed by our scaled controller, certain operations are less intuitive. Table 3 : Imitation learning evaluation. This table evaluates the system's ability to collect data and apply it to imitation learning tasks, followed by an evaluation of the performance in these tasks.  
 
 Appendix: We map the human hand pose data obtained from MediaPipe into joint positions of the teleoperated robot hand. This process is often formulated as an optimization problem [14] , where the difference between the keypoint vectors of the human and robot hand is minimized. The optimization can be defined as follows:  


Decision: The authors propose a low-cost system for teleoperation. The reviews largely agree the system is interesting, but there are many missing details with respect to implementation and efficacy.

The authors propose a low-cost teleoperation framework which can be used to collect data for many different types of robots, including humanoids. Hardware like this is essential to robot learning, and they attempt to make the system reproducible and open-source. This type of systems work is valuable to the community and I think it's worth endorsing, even if it does not immediately show exciting new learning algorithms in action. The quality of the writing is good. 

The authors provide some details about how to reproduce and build their system. They provide detailed feedback on how their method is an improvement over the state of the art.  They also provided a large number of key details during the rebuttal which remove some concerns about clarity and reproducibility, and they show interesting tasks like spraying and pipetting which would be very difficult for well-known humanoid teleop systems based on video capture.

Reviews: review: title: title: Review
paper_summary: paper_summary: The paper introduces ACE, a low-cost and versatile teleoperation system for bimanual dexterous manipulation. ACE employs two bimanual exoskeleton arms and hand-facing cameras to capture 3D hand poses and end-effector positions in real-time. The system's modular design allows for cross-platform operations, demonstrated across six different robot platforms. ACE can efficiently gather teleoperation data and train imitation learning policies for manipulation tasks.
main_review: main_review: Strengths:
1. It is an interesting exoskeleton system for teleoperation, which can be applied to multiple types of bimanual dexterous robots.
2. The system is low-cost and can be easily manufactured and distributed more widely.
3. Good experiment results on both humanoid robots and robot arms.


Limitations:
1. Although it is a cool design for switching between desktop and mobile versions, I’m curious if we really need it. Since the system is designed for “tele”operation, which is supposed to be used remotely, it shouldn’t really matter whether the system is fixed on a desktop or mobile, right?
2. The system relies on two hand-facing cameras to get the dexterous finger motion, which are mounted quite far from the human hand. Do the cameras collide with each other during teleoperation? For example, in Figure 1 Mobile version H1, it looks like the cameras has collision.
3. One important missing related work is [1]. Both systems are exoskeletons, but [1] focuses on collecting manipulation data directly without a robot, while this work focuses on teleoperation. I think both problems are important, but teleoperation seems to be a subset of [1], and [1] covers more use cases (teleoperation and collecting data without a robot). I hope to see the authors provide more discussion on this related work to justify the novelty of the proposed system.
4. It’s cool to see real-world bimanual manipulation results, but most of the tasks are pick-and-place style. What kind of new capability can this system provide compared to other teleoperation methods like VR headsets [2]? Also, it seems like the exoskeleton does not match with the robot’s joint design, which means it cannot perform joint mapping like ALOHA or GELLO. Does that affect teleoperation capability in more challenging tasks?
5. L249: “It is important to note that because we can collect a large amount of data in a short time, our GraspAndPlaceDolls policy has generalizability.” Are you trying to say that because data collection is fast, you can collect more data within the same amount of time, so your model performs better? But why does it also generalize better?
6. It would be nice to have a systematic measurement of the accuracy of the system. Figure 4 is good, but it contains compounding errors combined with the robot control error. Could you provide a number of distances with mean and standard deviation about the teleoperation system only? It would be helpful for readers to understand the quality of the data.
7. Is there any specific reason to use point clouds as input instead of images? It seems like an orthogonal problem to the main focus of this work (teleoperation system), which makes me curious about the reason for this experiment setup.

[1] AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild

[2] Open-TeleVision: Teleoperation with Immersive Active Visual Feedback
questions: questions: same as the limitation section.
limitations: limitations: 2
review_summary: review_summary: Overall, this is an interesting system for teleoperation. However, more discussion of prior exoskeleton work is needed. Additionally, the authors need to clarify the new capabilities this system provides compared to prior teleoperation methods.
title: title: Similar to existing solutions without sufficient novelty in algorithmic or implementation.
paper_summary: paper_summary: This paper presents a low-cost exoskeleton design to capture bimanual object operation data from human demonstration.
main_review: main_review: This paper presents a low-cost exoskeleton design to capture bimanual object operation data from human demonstration. The authors claim that their system is a better design with specific considerations regarding pose estimation and end-effector mapping. The authors did some preliminary experiments to verify its basic functionality in teleoperation various robot system. They also compared in simulation against GELLO with superior performance. They did not discuss in detail regarding the limitation of this work.
1.	The paper is written in general descriptive language without too much technical details, making this paper easy to read. However, considering this is already a very hot topic with various research interests, it is crucial for the authors to avoid using similar descriptive language and use more technical analysis and derivation to justify why the proposed system is better in what way and why.
2.	The authors raised a very interesting question at the beginning of this paper but did not provide any follow-up research or analysis into the particulars. And based on the current manuscript, I did not see any clear answer or in-depth analysis. 
3.	The strength of this work is probably the various hardware platform test they performed, which could be very challenging for most labs. The device takes an exoskeleton form, which is wearable by design. And the data collected is anthropomorphic instead of 2 finger gripper. 
4.	The weakness of this work is mainly on the lack of technical details, lack of in-depth analysis regarding why it performs better, lack of algorithmic related research or insights in the current format.
questions: questions: 1.	So, what is the answer to the question the authors raised in the beginning? “What key information must be captured to effectively perform  bimanual dexterous manipulation tasks?” Are the authors’ finding the same or different from other authors? It seems this paper gradually drifts from one theme to another while reading it.
2.	The majority of this paper lacks sufficient technical details for a thorough evaluation and most of the content are descriptive of the general process without further analysis or technical details. Please consider focus on at least one thing to explain or describe clearly what’s the advantage of the proposed work, why it works, how better it performs for robot learning task.
3.	Comparison with GELLO in simulation lacks sufficient technical details or in-depth analysis to justify why the result obtained. It is understandable if the authors do not have a hardware GELLO, so choosing to investigate through simulation. But this section lacks sufficient technical details on settings of the simulation parameters, which may vary significantly with a strong bias. And the authors did not provide any in-depth explanation why one performs better than the other, limiting the potential impact of this work.
4.	Table 2 is highly biased, and I wonder if the system requires a certain range of motion at the waist to be functional, even it can be used as a wearable device.
limitations: limitations: 2
review_summary: review_summary: No clear novelty but more of an increamental work withouth sufficient algorithmic or system contribution comparing to existing work.
title: title: Nice paper with very little learning
paper_summary: paper_summary: The authors present a low-cost bi-manual teleoperation device which is comprised of an exoskeleton for tracking the operator's wrist poses and two cameras attached to the exoskeleton for tracking the operator's finger poses. The main purpose of the device is to provide affordable hardware for collecting data for robot learning, e.g., imitation learning approaches.
main_review: main_review: The paper is well written and the presented hardware seems to be a valuable contribution to the robot learning community. The design decisions made by the authors are well explained and sound reasonable to me. The device is affordable (~600$) and seems to allow for accurate wrist pose tracking. Related work seems to be cited in in an adequate way.

Weaknesses of the paper in my opinion are:
- The paper is a hardware paper that uses learning only as a proof of concept. That means the authors showcase that their hardware can be utilized for collecting data that can be used for training imitation learning models. It really is a good paper but I don't think CoRL is the right conference for it. I'd prefer to see it at ICRA (or another conference that is more general or has a focus on robotics hardware) but it's up to the Area Chair to decide this.
- The experiments have a strong focus on the evaluation of the wrist pose tracking. The finger pose tracking has not really been evaluated.
- The authors mention that the precision of the wrist rotation tracking is limited. What does that mean? I wish the authors had discussed this in more detail.
questions: questions: - In l. 123 you mention that the two sources of information allow for tracking the hand pose relative to the world frame. Shouldn't that be the base frame of your device? While the transformation from the base of your device to the world frame is static for the desktop version, it's dynamic for the mobile version.

- Figure 3 (left) is too dark in my opinion. Would it be possible to increase the brightness?

- In l. 254+255 you say you have achieved 100% success rate for all tasks at the first step, which is not true according to Table 4. I suggest you either correct the sentence or modify it to make it more clear what you mean by "all tasks".

- Please elaborate more on why the precision of the wrist rotation tracking is limited and what could be done to alleviate this.
limitations: limitations: 2
review_summary: review_summary: As I said, it is a good paper but I don't think CoRL is the right conference for it because the focus of the paper is not on learning but on actual hardware that might be of interest to the learning community.