Bimanual Dexterity for Complex Tasks

Authors: kenneth  shaw
yulong  li
jiahui  yang
mohan  kumar
srirama ray liu
haoyu  xiong
russell  mendonca
deepak  pathak
Venue: robot-learning_org_CoRL_2024_Conference
Published: 2024-06-07 12:47:19

Abstract: To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However, creating such a bimanual teleoperation system with over 50 DoF is a significant challenge.  To address this, we introduce Bidex, an extremely dexterous, low-cost, low-latency and portable bimanual dexterous teleoperation system which relies on motion capture gloves and teacher arms.   We compare Bidex to a Vision Pro teleoperation system and a SteamVR system and find Bidex to produce better quality data for more complex tasks at a faster rate. Additionally, we show Bidex operating a mobile bimanual robot for in the wild tasks. Please refer to https://bidex-teleop.github.io for video results and instructions to recreate Bidex.  The robot hands (5k USD) and teleoperation system (7k USD) is readily reproducible and can be used on many robot arms including two xArms ($16k USD).

Main paper: 
 introduction: 
 General-purpose robots in human environments will need to perform a wide variety of challenging manipulation tasks. This ranges from intricate movements like screwing in small objects, to cutting vegetables, to operating tools to being able to move large objects like furniture. These are tasks built around humans, and correspond to activities that people can perform. The versatility of human hands in particular is essential for finer-grained tasks ranging from writing and creating art to typing on keyboards. Hence one approach to building such versatile robot systems is to use a hardware form factor that resembles humans with two arms each equipped with a dexterous multi-fingered hands.With the advent of data-driven machine learning methods and low-cost hardware there has been renewed interest in humanoids and dexterous hands. There is great promise for machine learning approaches to enable effective autonomous control for high-dimensional robot systems using large amounts of data [2, 3, 4, 5, 6] . A key question remains: how do we collect high-quality expert data for bimanual robots? Such a data collection system must be low-cost, easy to setup and use, low-latency and most importantly accurate enough. It should be effortless for the teleoperator to collect high quality data of robots performing complex tasks of interest to train robot policies.To address this problem, VR headsets have become increasingly prevalent due to their easy-to-use internal body tracking systems [7, 8] . However we find that wrist tracking is often jittery and the finger tracking is inaccurate. To mitigate this, SteamVR [9] uses LiDAR which provides less noisy estimates, but requires external tracking devices which doesn't allow for data collection for mobile robot setups. For even higher fidelity readings, there is work that uses motion capture and reflective marker-based approaches such as Vicon or Optitrack [10] but they are extremely expensive and difficult to setup. An aspect of motion capture technology that has been used in robot learning in recent years are wearable gloves [11, 1, 12, 13] for hand tracking which records human fingertip position using EMF sensors. We use the accuracte Manus Meta glove as part of our system. [14] For arm tracking, researchers in the robotics community have been recently using joint-level teleoperation for 2-finger grippers [15, 16] . Wu et al. [16] . They find that a low-cost 3D printed scaled teacher arm model that has the same kinematic link structure of a large robot arm can be used for accurate and effective teleoperation. These methods only provide one DOF of finger tracking instead of the twenty two plus DOF of the human hand. Our key insight is to develop a system that combines this joint-based arm tracking along with a Mocap fingertip glove to achieve accurate low-cost teleoperation of an arm and hand system.Our contribution is BiDex, a system for dexterous low-cost teleoperation system for bimanual hands and arms in-the-wild in any environment. To operate, the user wears two motion capture gloves and moves naturally to complete everyday dexterous tasks. The gloves captures accurate finger tracking to map motions naturally to the robot hands. The GELLO [16] inspired arm tracking accurately tracks the human wrist position and joint angles for the robot arm. The data collected by the system can be used to train effective policies using imitation learning, after which the bimanual robot system can perform the task autonomously. BiDex costs around $6k for a pair of Manus gloves and few hundred dollars for the arm teleoperation which works for many existing robot arms. Even including the robot arms ($8k xArms x2) and robot hands used in our demonstration ($3k LEAP Hand V2 x2), the total cost is under $30k. We compare the accuracy and speed of data collection to other commonly used systems: the VR headset and SteamVR. We release videos of our results and instructions to recreate the setup on our website at https://bidex-teleop.github.io 
related work: 
 Robot Arm Teleop Many common approaches to teleoperation include using joysticks, space mouses [17] , vision-based methods, [18, 19] , and VR headsets [20, 8, 7] which control arms with inverse kinematics. Joint based teleoperated control has been used in areas such as kinesthetic teaching, Brantner and Khatib [21] , ABB YuMi [22] and Da Vinci Machines [23] , and recently [24, 25] introduced a low-cost version of this with mirroring Trossen Robot arms. GELLO [16] uses light and inexpensive teacher arms that are 3D printed to control full size robot arms, a system we use in our BiDex due to its lost-cost, accurate, portable design.The high dimensionality makes tracking human hands particularly difficult. To control robot hands, many vision-based techniques such as [26, 27, 19] do not require specialized equipment but are not that accurate. Shadow Hand developed a professional system that uses SteamVR and two gloves to control two Shadow Hands [28] . Recently, bimanual robot hands and tracking have become accessible for academic labs. Dexcap uses LEAP Hand [1] and tracks the human with gloves and a SLAM-based robot camera. [11] Hato uses a VR headset and controller to control two 6 DOF Psyonic Hands [29, 30] . A key question in controlling robot hands is how to map the human hand configuration to robot hand joints. These papers introduce inverse kinematics based methods that optimize pinch grasps between the human and robot hands [11, 26, 19, 27] .Motion Capture Motion capture and graphics contributions often are useful in the robotic teleoperation domain. Outside-in mocap approaches use external sensing technology to track the human body or other objects in the scene. SteamVR uses external lasers and worn wireless laser receivers. [9] Vicon-based systems use reflective balls and external cameras to track. Inside-out approaches such as XSens [31] or Rokoko suit rely on IMUs on the body but these often drift over time and require recalibration [32, 29] . For hand data, many vision-based approaches such as Frankmocap [33] return MANO [34] parameters which can be converted to robot hand joint angles.Learning from Expert Demonstrations Recently the robot learning community has seen notable success in learning from demonstrations driven by the development of imitation learning algorithms [35, 36] . Complementing these advances, significant efforts have been made to scale up robotic datasets to facilitate more capable robotic systems [37, 38, 39, 40] . Despite these efforts, acquiring robotics data remains an expensive and challenging endeavor. To address these issues, developments in low-cost hardware have been instrumental in democratizing access to robotic technology, enabling more widespread research and application [41, 24, 25, 42] . However, these systems are primarily focused on simple gripper functionalities; and the challenge of achieving more intricate dexterity and intuitive control in robotic systems motivates our bimanual dexterous teleop system. 
bimanual robot hand and arm system: 
 We present BiDex, a system that allows any operator to effortlessly teleoperate a bimanual robot hand and arm setup. BiDex is designed to be exceptionally precise, affordable, low-latency, and portable, enabling control of any human-like pair of dexterous hands, even those with over 20 degrees of freedom. It achieves accurate tracking of the human hand using a Manus VR glove-based system [14] and human arm tracking through a GELLO-inspired system [16] . We outline the process for sending commands and collecting data with bimanual hands in Alg.1. Importantly, our solution functions seamlessly in both tabletop and mobile environments, as it requires no external tracking devices and is highly portable. In Section 5, we demonstrate that our system is highly intuitive, precise, and cost-effective compared to widely used methods today, such as VR headsets and SteamVR, across two different pairs of open-source robot hands, LEAP Hand [1] and LEAP Hand V2 [43] .A hand tracking system must deliver accurate, low-latency joint information for the human hand, which has over 20 degrees of freedom. Many current vision-based tracking solutions, such as those using FrankMocap [33, 44] or VR headsets, often face significant inaccuracies due to occlusions and varying lighting conditions, as discussed in Section 5. In contrast, recent motion capture gloves that utilize EMF sensors provide significantly more accurate tracking without being overly expensive. They avoid the occlusion issues common in vision-based methods and offer detailed data on the skeletal joint structure of the human hand. Additionally, these gloves can be worn comfortably without hindering movement. In BiDex, we opt for the Manus Glove [14] , which has demonstrated reliable tracking performance without overheating or suffering from calibration problems as seen with alternatives like the Rokoko gloves [32, 29] . However, mapping this data from the human hand while task not completed do Set joints of Hl using ql t and Hr using qr t 16:end while 17:Add T → D 18: end for 19: return Data buffer D If the kinematic structure of the robot hand is roughly human-like, one approach would be to directly map the joint angles from human finger joints to those of the robot hand. Although these gloves do not provide true joint angles, they can compute them using an inverse kinematics solver applied to a standard human skeleton. However, if the robot fingers differ significantly in size and proportions from human fingers, the resulting motions may not align properly. The human thumb, in particular, has a complex joint configuration that many robot hands fail to replicate accurately, complicating intuitive thumb control. This can lead to inaccuracies in pinch grasps, negatively impacting the reliability of task performance, as effective manipulation heavily depends on the relative positions of the fingertips.Previous work [26, 19] has addressed this challenge by ensuring that pinch grasps are consistent between human and robot hands. Wang et al. [11] demonstrated that effective mapping can be achieved by optimizing the joint positions of the fingertip and penultimate joint (DIP) on each finger in relation to the wrist, ensuring similarity between the human and robot hands through an SDLS IK solver [45] . We employ the Manus gloves [14] using a similar inverse kinematics approach, which allows for precise pinch grasps and proper thumb positioning.Our system must accurately track the human wrist pose to control two robot arms. Traditionally, various methods have been developed by both the motion capture and robotics communities. However, many of these approaches rely on calibrated external tracking devices which either are costly or have high latency. These external tracking devices are also non-portable, making it hard to scale to mobile systems. Instead, we leverage key insights from Zhao et al. [15] , Wu et al. [16] which both use lighter teacher arms attached to the human arm to control a robot arm and hand system. Specifically we follow the GELLO system from Wu et al. [16] to teleoperate a full size robot arms. A key question is how to mount this arm-tracking system on a human wrist and hand. If the robot hand is mounted on the arm in a human-like way, then the glove needs to be mounted in a human-like way on the teacher arm to match. However, this orientation means that the human arm will be parallel with the teacher arm and constantly collide with it. This is jarring for the operator and uncomfortable.In BiDex, we mount the robot hands underneath the arms in the same orientation as if it were a gripper as in Figure 2 . When mirroring this in the teacher arm, the human arm and teacher arm output are perpendicular to each other and do not collide which is more comfortable. Because of the weight of the motion capture glove, we must adjust the teacher arm to be more robust. This includes adding a strong bearing to the base joint of the teacher arm and adding rubber bands to bias additional joints back to the center of the joint range.Tabletop Manipulation For our tabletop setup, the robotic arms are positioned to face each other similar to Zhao et al. [15] while the GELLO teaching arms mirror this configuration. Compared to a side-by-side configuration like in Wang et al. [11] , this setup has three main benefits: 1) the human operators avoid collisions with the teacher arms; 2) the setup allows better visibility of the workspace, which will be otherwise occluded by a side-by-side robot arm configuration; 3) and finally, the robot arms have a wider shared workspace.Mobile Manipulation BiDex operates without the need for external tracking systems and is lightweight, making it well-suited for mobile settings. The teacher arms are mounted on a compact mobile cart. For the mobile robot, we attach two robot arms to an articulated torso capable of moving upward to reach high objects and downward toward the ground, similar to the PR2 [46] , as shown in Figure 2 . The robotic assembly, which includes the arms and torso, is mounted on an AgileX Ranger Mini, allowing for movement in any SE(2) direction [47, 48] . A secondary operator uses a joystick to control the mobile base and manage task resets. 
baseline teleoperation approaches: 
 Vision based VR Headset In recent years, the accessibility of low-cost VR headsets using multicamera hand tracking has made them popular for teleoperation such as in [7, 8] As a baseline we use the Apple Vision Pro which returns both finger data similar to MANO parameters [44] and wrist coordinate frame data. The finger data is used in the same way as with BiDex through inverse kinematics-based retargeting and commanded onto the robot hands. The wrist data is reoriented, passed through inverse kinematics and the final joint configuration is commanded to the arms.SteamVR Tracking SteamVR, commonly used in the video gaming community has also seen recent interest in the robotics community from industry [28] and academia alike. [12, 49] It uses active powered laser lighthouses that must be carefully placed around the perimeter of the workspace. Wearable pucks with IMUs and laser receivers are worn on the body of the operator. In our experiment the operator wears one tracker on each wrist and one tracker on their belly. The wrist position is determined relative to the belly pose, mapped to the robot arm, and the joint angles are computed using inverse kinematics. The hand tracking gloves are the same as in BiDex. and discarding it into a bin. Bottom: Grasping a chair and moving it to align with a table.Leap Hand LEAP Hand, introduced by Shaw et al. [1] is a low-cost, easy-to-assemble robot hand with 16 DOF and 4 fingers. LEAP Hand introduces a novel joint configuration that optimizes for dexterity as well as human-like grasping. We use this hand for many experiments in the paper as it is a readily available dexterous hand available for comparison studies.We would like a hand that is smaller and more compliant than LEAP Hand. LEAP Hand V2 is crafted to mimic the suppleness and strength of the human hand with fingers that have a 3D-printed flexible outer skin paired with a sturdy inner framework resembling bones. These fingers do not break but instead bend and flex upon impact. We also introduce an active articulated palm which integrates two motorized joints, one spanning the fingers and another for the thumb, enabling natural tight grasping. LEAP Hand V2 contains 21 degrees of freedom and is sized to resemble a human hand, easy to assemble and is economical. Because of the human-like size and kinematics, it is easy to retarget to and can complete many more dexterous tasks successfully.Tabletop In Handover, the robot picks up a pringles can and passes it from its right hand to its left hand in the air. In Pour, the robot pours from a glass bottle in one hand into a plastic cup held by the other hand. In Tabletop Cup Stack the agent stacks one cup into another cup in the other hand.Mobile In Transport Box, the agent moves a box from one table to another table using two hands. In Mobile Chair Push, the agent needs to grasp a chair, and then align it with a table. In Clear Trash, the robot clears trash from the table into a dustbin. We visualize the chair push and clear trash tasks in Figure 3 . 
results: 
 We investigate BiDex teleoperating in both the tabletop scenario and in the mobile in-the-wild scenario against a few baselines. For these comparisons, we use LEAP Hand by Shaw et al. [1] because it is an open source easy to assemble robot hand that is readily attainable by any robotics lab. We also use BiDex with the more recent LEAP Hand v2, which is made of a combination of rigid and soft material and capable of performing more complex tasks. Table 2 : Mobile Teleoperation: Completion rate and time taken averaged across 20 trials using a mobile bimanual system with LEAP Hand [1] , for different tasks. BiDex is versatile and compact enough to be adopted to successfully collect data for mobile tasks.BiDex provides more stable arm tracking. The teacher arm system makes BiDex highly reliable, with minimal jitter, low latency and high uptime, making it ideal for arm tracking. The teacher arm is lightweight and doesn't impede the user more than the gloves' weight. The kinematic feedback from arm resistance is subtle but helps operators navigate around arm singularities intuitively. As shown in Tables 1 and 2 , BiDex achieves a higher completion rate in less time for teleoperators.In contrast, the Vision Pro often experiences jittery arm tracking, complicating the teleoperation of more demanding tasks. While low-pass filtering can mitigate this issue somewhat, it introduces undesirable latency. Occasionally, the system may stop functioning entirely, which can be disconcerting for users.The SteamVR system offers wireless connectivity, allowing users to be untethered, and it generally provides accurate tracking. However, it can experience brief episodes of high latency or disconnections every 5-10 minutes, which can be jarring. Notably, the SteamVR system cannot be used in mobile settings due to the need for external tracking lighthouses to be set up around the teleoperation environment.BiDex provides more accurate hand tracking. With BiDex, fingertip tracking is highly accurate when using the Manus glove. When mapping to different robots, only minor adjustments to inverse kinematics are required for operators with varying hand sizes. The accuracy of abduction and adduction at the MCP joint remains dependable across different conditions. These benefits are particularly crucial in LEAP Hand V2, where precision is essential for executing more complex tasks. [15] using data collected by BiDex and find that our system can perform well even in this 44 dimension action space. This demonstrates that our robot data is high quality for training robot policies.In contrast, the Vision Pro can struggle with hand size variability under different lighting conditions, making the retargeting process to robot hands more challenging. Additionally, finger abduction and adduction estimates can be impacted by occlusions, which complicates the performance of intricate tasks. The latency is noticeable and can pose a significant issue for teleoperation. To verify that the data that is collected by our system is high quality and useful for machine learning we train single task closed loop behavior cloning policies.Specifically, we train an action chunking transformer from [15] with a horizon length of 16 at 30hz using pretrained weights from [50] on around 50 demonstrations. The state space is the current joint angles of the robot hand and the images from the camera. The action space in the case of LEAP Hand is 16 dimensions for each hand and 6 dimensions for each arm for a total of 44 dimensions. During rollouts, the behavior of the policies are very smooth, exhibiting the high quality of the teleop data.In tasks such as the YCB Pringles can [51] handover, we even see good generalization of the policy to different initial locations of the can.To push BiDex to its dexterous limits, we use LEAP Hand V2: an extremely dexterous 21 DOF hybrid low-cost hand which is explained in Section 4.2. To do this, we show a variety of very challenging tasks as in Figure 1 and Figure 4 . These tasks include pouring, scooping, hammering, chopstick picking, hanger picking, picking up basket, drilling, plate pickup and pot picking. In these experiments we find that BiDex scales well to this high DOF hand and it feels very natural to control this soft-rigid robot hand. We provide Table 4 and video results on our website at https://bidex-teleop.github.io 
discussion and limitations: 
 In this paper, we introduce BiDex, a portable, low-cost and extremely accurate method for teleoperating a bimanual, human-like robot hand and arm system. We demonstrate the system's applicability to both a tabletop and a mobile setting and show its efficiency in performing bimanual dexterous tasks in comparison to alternative approaches including SteamVR and Vision Pro. Nevertheless, our BiDex is not without limitations. Due to the lack of haptic feedback, the human operator has to rely on visual feedback for teleoperation and cannot feel what the robot hand is feeling. Additionally, they cannot exert intricate force control and can only control the kinematics of the robot hand and arm which can make it challenging for fine-grained manipulation tasks. A promising direction in the future would be to integrate haptic feedback into our system which will unlock further potential for collecting extreme dexterity data. and the Apple Vision Pro system. Each participant performed the Pringles handover task over 13 10 trials, following a 3-minute practice session with each system. We collected data on average 14 task completion times and success rates, as well as users' ratings (on a scale from 1 to 5) regarding accuracy, responsiveness, ease of use, and their confidence in each system. The results are summarized 16 in Fig. 4 . and the Apple Vision Pro. In particular, we collected 100 demonstrations on the Pringles can handover 27 task using both systems, and trained policies with different numbers of demonstrations. We then 28 evaluate each policy for 10 trials at roughly the same starting poses. We summarized our results in 29 Fig. 2 .In general, we found that policy performance scales with the number of demonstrations, an unsurpris- (c) User feedback on accuracy, responsiveness, ease of use, and confidence in using each system.  
 
 Appendix:  


Decision: **Pre-rebuttal**

This paper receives a mixed review from three reviewers. Overall, all three reviewers acknowledge the potential value of the hardware system to the robot learning community.

Strength mentioned:
- Strong relevance (**vkX4**).
- Versatility of the system (**vkX4**, **ew2f**).
- Demonstration of the utility of the collected data (**vkX4**).
- Clarity of presentation (**kK4n**, **ew2f**).

Weaknesses mentioned:
- Limited / unclear technical novelty (**vkX4**, **ew2f**).
- Lacking in evaluation quality (e.g. accuracy, latency, etc.) (**vkX4**, **ew2f**).
- Evaluation with lay users (**vkX4**, **ew2f**).
- Lacking in comparison with other imitation learning approaches / data collection methods (**vkX4**).
- Topic fit to CoRL (**kK4n**).

---
**Post-rebuttal**

This paper initially received one strong accept, one weak accept, and one weak reject. All the reviewers retain their original rating after the rebuttal.

After reading the paper, reviews, and the rebuttal, AC considers this paper a borderline case. Particularly, AC acknowledges the merits of the hardware contribution and the substantial engineering effort for putting together the system. Meanwhile, AC agrees with following shortcomings:
- Lack of rigorous evaluation.
- Lack of scientific novelty.
- Limited imitation learning results.

However, considering the recent research landscape and the growing interest in the teleoperation of bimanual dexterous setups, AC recommends to accept the paper with the belief that the paper will be of sufficient interest to the CoRL audience.

AC would also like to remark the importance of the user study and encourage the authors to include the new user study (from the rebuttal) to the main paper if the paper is eventually accepted.

Reviews: review: title: title: Cool new hardware tools for the dexterous robotics community
paper_summary: paper_summary: The authors present BiDex, a low-latency, low-cost, and portable bimanual dexterous teleoperation system. The system combines motion capture gloves for hand tracking with lightweight teacher arms for arm control, offering an alternative to existing methods like SteamVR and Vision Pro.
main_review: main_review: The paper is well-written and clearly explained. The figures and video demonstrations are quite convincing in demonstrating the need for two-arm multi-finger teleoperation systems. The authors showcase the system's versatility through both tabletop tasks (handover, cup stacking, bottle pouring) and mobile manipulation scenarios (chair pushing, box carrying, trash clearing).

The authors claim that BiDex is "exceptionally precise", but don't offer quantitative data to support this claim. They also assert that using the system is "highly intuitive"; some user feedback studies could be useful in supporting this claim.

The experimental results, while promising, are quite limited. The paper would be significantly improved with additional precision and latency testing, as well as statistical analysis of the results. The results table doesn't report standard deviations or perform a statistical analysis of the outcomes. Crucially, the paper doesn't specify who is operating the system during these experiments. It's very important that it's not the authors but a selected group of participants to avoid potential bias.

In terms of scientific novelty, the paper is somewhat limited, as it mainly assembles pieces from existing works (motion capture gloves, teacher arms).
questions: questions: See above
limitations: limitations: 3
review_summary: review_summary: Overall, in combination with the promised release of CAD models and source code, I think this paper would be valuable to the robotics community, despite its limitations. It provides a practical solution to bimanual dexterous teleoperation that could accelerate research in this area. However, the authors should address the lack of rigorous validation for their claims and expand their experimental analysis to strengthen the paper's contribution.
title: title: Bimanual Dexterity for Complex Tasks
paper_summary: paper_summary: This paper introduces a low-cost portable teleoperation system for bimanual dexterous manipulation using robot arms and hands. The key components are motion capture gloves for hand tracking and lightweight "teacher arms" for arm tracking. The authors compare BiDex to baseline teleoperation methods using VR headsets and SteamVR, demonstrating improved performance on several manipulation tasks in both tabletop and mobile robot settings. They also show the system can be used to collect data for training imitation learning policies.
main_review: main_review: Strengths:
1. The proposed BiDex system addresses an important need for affordable, accurate teleoperation of high-DOF bimanual robot systems. 
2. The evaluation includes both tabletop and mobile manipulation scenarios, showing the versatility of the system.
3. The authors demonstrate the utility of the collected data by training imitation learning policies that achieve reasonable performance.

Weaknesses:
1. The technical novelty is somewhat limited - the key components (motion capture gloves, teacher arms) have been used in prior work. While the integration is valuable, the paper could benefit from a clearer articulation of the novel technical challenges overcome in combining these components for bimanual control. More discussion of the design choices and engineering challenges would strengthen the contribution.
2. The evaluation is primarily focused on task completion rates and times. Quantitative measures of tracking accuracy, latency, and precision of object manipulation would provide a more complete picture of the system's capabilities. Additionally, a user study with participants not involved in the system's development would help validate the claimed intuitiveness and ease of use.
3. The imitation learning results are preliminary - more extensive evaluation and comparison to other policy/data acquisition methods would be valuable. While the reported success rates are promising, more comprehensive experiments comparing to other learning approaches or data collection methods would better contextualize the contribution. Learning curves, analysis of policy generalization, and comparisons to policies trained on data from other teleoperation systems would significantly strengthen this aspect of the paper.
questions: questions: 1. Can you provide more detailed analysis of the precision/accuracy of the arm and hand tracking, beyond just task completion metrics?

2. What are the primary failure modes or limitations of the BiDex system? Are there certain types of tasks or motions that are particularly challenging?

3. The paper mentions that BiDex enables "extreme dexterity", but the tasks shown are fairly standard manipulation. What is precenting more complex dexterous tasks that truly push the limits of the system?
limitations: limitations: 3
review_summary: review_summary: Overall, this paper presents a useful system integration that could benefit the robot learning community by enabling more widespread collection of dexterous manipulation data. While the technical novelty is not extremely high, the potential impact and practical utility of the system make this a valuable contribution. The paper would be strengthened by more rigorous evaluation and analysis.
title: title: Good paper with very little learning
paper_summary: paper_summary: The authors present a low-cost bi-manual teleoperation device which is comprised of two teacher arms for tracking the operator's wrist pose and Manus gloves for tracking the operator's finger poses. The device can be mounted to a table or on a mobile base, e.g., for teleoperating a mobile robot. In the latter case a second operator must control the pose of the mobile base. The device can be utilized for teleoprating a dexterous bimanual robot and/or to collect demonstration data, e.g., for 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 (~5k$) and seems to allow for accurate wrist pose and finger pose tracking. Related work seems to be cited in in an adequate way. 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 because the authors do not solve any learning problem. A conference like ICRA (or another conference that is more general or has a focus on robotics hardware) is a much better fit for the paper but it's up to the Area Chair to decide this.

More minor weaknesses/suggestions:
- I felt like Section 3.2 was a bit too short. More details on the differences to GELLO (e.g., a picture comparison) would improve the paper.
- The authors could have discussed if it’s possible to use other end-effectors on the robot and teacher side. If my robot has only grippers, the Manus gloves are not really necessary and could be replaced by a cheaper input device. What would be necessary to modify the setup for controlling robots that have only grippers?
questions: questions: - The video on the homepage does not play. I tested it in three different browsers (firefox, chrome, edge). Only downloading the video and then playing it in VLC seems to work. Please re-check the encoding of the video.

- Typo: "biamanual" in caption of Figure 2
limitations: limitations: 3
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.