TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning
Authors: Jimmy Wu
William Chong
Robert Holmberg
Aaditya Prasad
Yihuai Gao
Oussama Khatib
Shuran Song
Szymon Rusinkiewicz
Jeannette Bohg
Venue: CoRL 2024 (2024)
Keywords: mobile manipulation
imitation learning
holonomic drive
TL;DR: We design an open-source holonomic mobile manipulator and show that it is capable of performing a variety of household mobile manipulation tasks in a real apartment home.

Abstract: Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning. In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform a variety of common household mobile manipulation tasks.

Tidy Bot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning
Jimmy Wu', William Chong", Robert Holmberg', Aaditya Prasad?, Yihuai Gao?,
Oussama Khatib², Shuran Song', Szymon Rusinkiewiez', Jeannette Bohg² PPiineeto University Stannford University *Dexterity
Abstract: Exploiting the promise of recent advances in imitation learning for mo- bile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base be fully holonomic, able control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipu- lation tasks, eliminating the kinematic constraints that create complex and time- consuming motions nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imita- tion learning. In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform variety of common household mobile manipulation tasks.
Keywords: mobile manipulation, imitation learning, holonomic drive
Open fridge
Wipe countertop
Load dishwasher
TidyBot++
Take out trash
Water plant
Load laundry
Figure 1: We develop open-source mobile manipulator with holonomic base (left), and show that it can perform a variety of household tasks in real apartment home (right).
Introduction
Imitation learning from real- world data is starting to show very promising results in robotics for both
fixed-arm robots [1, 2, 3, 4, 5, 6, 7] and mobile manipulators [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. However, one key bottleneck is the availability of data. Unlike in natural language processing, which can train readily-available data from the internet, real-world data for training robot policies is much harder to come by. As result, scaling data collection of robotic tasks has
8th Conference Robot Learning (CoRL. 2024), Munich, Germany.

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become a high-interest research direction. Recent efforts have collected large robot learning datasets to address this [22, 23, 24, 25, 26, 27]. These datasets were largely collected on fixed-arm robot setups. However, to bring robots to their full potential, mobility is important as will enable many more tasks in realistic household settings [8].
We believe that one reason there are so few data collection and learning efforts in mobile manipu- lation is the lack of suitable research hardware. Existing commercial options for mobile bases are often tailored towards industrial or warehouse use cases, and may be ill-suited for household envi- ronments due to their large size. They are also often expensive and are typically subject to kinematic constraints.
In this work, we propose an open-source design for mobile base designed to carry robot arm sized for use in household environments. In addition to being inexpensive, flexible, and easy to assemble, our base is holonomic: able to independently move in any of the three degrees of freedom (DoFs) on the ground plane-(z, y. 0)-at any time. We argue that this is an important advantage for more intuitive teleoperation, and greatly increases the ease of acquiring large amounts of training data for real-world imitation learning.
Nonholonomic robots, such differential drive (wheelchair-like likke) or Ackermann drive (car-like) platforms, have constrained degrees of freedom in their motion. The most notable consequence of this is that they cannot move sideways. For example, cars cannot directly drive sideways into a street-side parking spot and must execute a multi-step parallel-parking maneuver.
In contrast, holonomic robots have no kine- matic constraints and can simultaneously and independently control all three degrees of free- dom. An example of holonomic vehicle com- mon in everyday life is an office chair, which can be smoothly pushed or rotated in arbitrary directions. This is enabled by the design of the caster wheels (Fig. 2), which have an offset be- tween the vertical axis of the swivel mechanism and the roll axis of the wheel. This offset is a crucial design feature of casters and is what makes the office chair fully holonomic. It creates lever arm that causes the wheel to trail behind the vertical axis of the swivel as the chair moves, automatically aligning the wheels to the direction of movement. Without a caster offset, the vehicle would be omnidirectional (capable of moving in any direction) but still nonholonomie, as the wheels have to be manually aligned face the direction of desired motion before the vehicle can begin moving. Overall, holonomic drive is preferred for maximum maneuverability.
Figure 2: A simplified illustration of caster wheels on a holonomic base.
Our holonomic base uses powered-caster drive mechanism [28]. It is driven by four motorized casters, and can be thought of as a motorized office chair. The ability to steer all four wheels makes the base omnidirectional, and the caster offset makes the base holonomic, allowing it to instantaneously accelerate in any direction does not need first align the wheels the direction of motion.
A holonomic mobile base enables easier telcoperation and kinesthetic teaching for collecting im- itation learning data. Everyday tasks such as opening doors and cabinets often require sideways motions of the mobile base to improve the workspace of the arm during execution. This useful motion not immediately available with differential drive base. Instead, the robot has to replan vehicle trajectories to satisfy nonholonomic constraints, which costs extra motion and time with no added value to the task. A holonomic mobile base, on the other hand, can be much more reactive. It can be moved arbitrarily in any direction no matter the current configuration, allowing an operator to make fine adjustments to the positioning of the base.
A holonomic base also useful for policy learning and inference. Recent real-robot imitation learn- ing works have converged on the use of position representations, as they are more stable and less

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noisy compared to velocities. However, nonholonomic mobile base can only be controlled in ve- locity mode [8, 16]. A holonomic base, on the other hand, can be directly commanded to go to a task space position (z, y, d) in a repeatable manner, as it can independently control all DOFs with no constraints. In our experiments, we show that we can indeed train high-performing policies for our robot across several mobile manipulation tasks in a real apartment home. Additionally, we show that policies can be learned more easily with data collected from holonomic base compared to nonholonomic one.
To facilitate easy data collection with our new mobile manipulator, we also develop a mobile phone telcoperation interface. The interface uses the WebXR API [29] to stream the real-time 6-DoF pose of the mobile phone to a computer, which maps the phone motion to corresponding motions of the mobile base
via low-level control. WebXR is supported on most modern Android and iOS phones, so interface does not require purchase of separate teleoperation device. In experiments, we use this teleoperation interface to collect data for training our policies.
Our holonomic mobile base is low-cost ($5-6k 6k USD) and designed from the ground up to optimize for mobile manipulation research productivity. We will fully open source all aspects of this system, including the hardware design, mobile phone teleoperation interface, policy learning setup, and low- level controller. We will also create documentation webpage for the mobile base, including bill of materials (BOM), hardware assembly guide with videos, and 3D CAD files. We believe these components can help democratize access to highly maneuverable mobile manipulators, increase ease and practicality of mobile manipulation data collection, and improve research reproducibility by providing standardized and reusable robot platform.
Our key contributions in this work are thus: (I) an open-source design for holonomic mobile manipulator, (2) mobile phone teleoperation interface for easily collecting data with the mobile manipulator, and (3) demonstration that our system is capable of learning policies. Please see project page at http://tidybot github io for documentation, code, and qualitative videos of our robot in action.
2 Related Work
Mobile manipulation hardware platforms. Recently, there has been an increased interest in equipping manipulation robots with mobility to demonstrate their full potential for variety of espei-lspy in domestic settings [8. 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41J. These works utilize a number of different mobile bases which we compare our base in more detail in Tab. 1. For example, several projects the Tiago mobile manipulator, and learn policies with combination of RL, simulation, and human motion transfer [42, 43, 44, 30, 45). Tiago is holonomic through its use of mecanum wheels, but the wheel comes with a few drawbacks. Due to the multiple small rollers on the wheel, the contact point with the ground is discontinuous leading to vibrations during operation. They also provide less traction and have poorer ability to traverse door thresholds and other inevitable household clutter compared of bases uses differential drive, which prevents them from freely navigating every degree of free- dom [46, 36, 8). An example of such robot is Everyday Robots' mobile manipulator, which often appears in Google's 's robot learning papers scalable mobile manipulator platform [36, 47, 48). This robot is neither open source nor purchasable by the public.
conventional wheel. Another common category
While the above works use wheeled bases, several works attach a robot arm to quadruped and uti- lize the combined system as a mobile manipulator capable of traversing terrains that wheeled robots cannot access [35, 37, 49, 50]. Though this combination expands the set of navigable environments, these quadrupeds nonholonomic since they are kinematically constrained by the orientation and placement of their limbs and feet.
address the lack of roboties data for learning ma- nipulation policies, several works have developed data collection platforms. The majority of these platforms are built for fixed-arm setups [51, 52, 25, 53]. For example, the DROID dataset [25] was collected on standardized setup with an arm mounted on portable table. The authors use an
Data collection for mobile manipulation.
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THE
Portable power station
Caster module
Power distribution
T-slot aluminum frame
SLA battery
Computer
Figure 3: Our mobile base is designed to be modular and easily reconfigurable. It has very few components and can be assembled in 1 to days.
Oculus controller to teleoperate the robot. However, this controller must remain in view of four IR receivers which can lead to unexpected motion if the controller moves out of view and back. Similar to work, RoboTurk [51, 54] used mobile phone teleoperate fixed-base robot arms which is much more flexible solution and does not require purchasing dedicated teleoperation device. However, their system suffers from drift as they only rely on IMU measurements and do not use the camera. MART [53] and MOMART [55], which extend RoboTurk to multi-arm and mobile ma- nipulation, respectively, suffer from similar shortcomings and have not been demonstrated on real robots. our work, we use the WebXR API [29], which combines IMU data with visual odometry based on the phone's camera to mitigate drift. TeleMoMa [45] is a teleoperation framework that supports multiple teleoperation interfaces and three commercially-avaiaaee detailed comparison to low-cost base, see Tab. 1). One of the supported teleoperation devices is mobile phone app based on ARKit, similar to what we use. Our interface is based on WebXR, which leverages ARKit on iPhone but works on Android as well.
high-cost robots (for
There are several works that propose data collection devices that are hand-held by the human demon- strator [52, 11] but in this case the demonstrator does not get direct feedback on whether a demon- stration is kinematically feasible by the robot. Of those approaches, Dobb-E
proposes a low-cost reacher-grabbe stick with mounted iPhone to record data. The authors then train visuomotor poli- cies on this data that are deployed on a differential drive Stretch robot [46]. Mobile ALOHA [8] is a dual-arm mobile manipulation platform capable of performing an impressive array of household tasks. However, the robot's differential drive base and large footprint limits its maneuverability, and arms are not able to reach the ground. Furthermore, the teleoperator strapped to the back of the platform far away from the end effectors, which can make it hard teleoperate precise actions. For our system, the teleoperator can freely walk around the scene and get very close when precision is required.
Hardware Design
We designed this mobile base concept from the ground up to optimize for mobile manipulation research productivity. It is simple, low-cost ($5-6k), and modular (Fig. 3). The core is the drive system, which is based on readily available components from the FIRST Robotics Competition (FRC) [56] ecosystem. basic frame built out of aluminum T-slot extrusions carries the four mo- torized caster modules that are powered through a fused power distribution panel by a sealed lead acid (SLA) battery. There many similar components in the FRC ecosystem that could be used to build similar systems. The large and active community of FRC users and vendors ensures that components are well-documented and readily available.
Our drive system is derived from set of SDS MK4 swerve modules [57] widely used in FRC. The swerve modules are very similar to a caster, with a wheel that can be actively steered and driven, except they have no caster offset and thus create nonholonomic base. The modules use two motors with integrated encoders and CAN bus controllers, one for steering and one for driving. Additionally, there is an absolute encoder mounted directly to the stecring axis to measure the steer

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position, and thereby eliminate the need for startup homing motion. A USB-to-CAN adapter is used to communicate with the motors and encoders through CAN bus.
We modify the MK4 swerve module to create a holonomic base design by introducing caster offset using a minimal number of modifications the stock swerve module: 2 custom 3D-printed wheel mounts and custom machined shaft. The wheel mounts can be printed with PLA filament standard FDM 3D printer, and the shaft can be easily ordered from online machining services like Xometry using our provided CAD file and specifications. All other parts of the off-the-shelf kit are directly reused.
To complete our mobile manipulator, we add a mini PC (Intel NUC) and the Kinova Gen3 arm. Power for compute, manipulation, and peripherals is provided by high-capacity (768 Wh), fast- charging (0-100% in 70 minutes) portable power station (camping battery) with AC power outlets. The portable power station (8.6 kg) and SLA battery (6 kg) serve counterweights to prevent the base from tipping over. Note that it would be possible to build electrical circuitry to use only one battery for more space efficiency, but we opted for separate batteries as it provides more flexibility and makes setup much easier.
Our open-souree design is highly customizable: the frame can be easily modified to support mount- ing of different arms or even multiple arms, and many additional sensors such as cameras, micro- phones, etc., can be easily mounted and powered, to suit the research being done.
3.1 Powered-caster vehicle kinematics
For our low-level controller, we model the kine- matics of the mobile base largely following the formulation of the powered-caster vehicle (PCV) in Holmberg and Khatib [28], which de- scribes the mapping between the joint space and the operational space (x,, y, 0) of the base.
h
B
b
bx
9
Each caster module is modeled with two revo- lute joints: a steer joint and a roll joint. The steer joint determines the wheel's steering an- gle, while the roll joint measures the wheel's rotational movement. We have an incremental encoder in each motor, well as an absolute encoder on the steer axis of each caster. With these encoder readings, we can calculate the joint positions and velocities using gearbox kinematics.
by
Figure 4: Isometric and top views of a simplified caster, showing the caster offsets b, and by, wheel radius r, steer and roll joints & and p. and caster module placement (h, 3) from the base origin.
The main difference from the original PCV formulation [28] is that instead of a single caster offset our caster module has a two-dimensional offset: traditional longitudinal b, offset as well as a small lateral by offsst (Fig, 4). This mainly comes as a byproduct of our design criteria to minimize the number of custom parts needed. It would be possible to have no lateral offset by by designing more custom parts, or designing new caster from scratch, but this would significantly lower the accessibility of our design, as creating custom parts requires detailed design work and is very costly to manufacture at low quantities.
3.2 Design principles
Our holonomic mobile base is specifically designed and optimized for robot learning research. This objective shapes our key design principles:
Research flexibility. We would like users to be able to customize the robot to meet their own spe- cific needs. The frame designed using standard aluminum T-slot extrusions, providing flexibility to easily adjust the dimensions and shape of the frame. We use portable power station (camping battery) with four AC power outlets, providing flexibility power other kinds of robot arms well computers of various operating voltages. Components can be simply plugged in directly, with-
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Table 1: Mobile base and mobile manipulator comparison
Specification
Ours
Stretch
Tracer
Ranger Mini
Husky
Fetch
Tiago
Holonomic
Yes
No
No
No
No
No
Yes
Omnidirectional
Yes
No
No
Yes
No
No
Yes
Swappable arm
Yes
No
Yes
Yes
Yes
No
No
Footprint (cm)
50x54
33x34
57x69
50x74
67x99
51x56
54x54
Weight
34 kg
2g
30kg
63 kg
50
113 kg
k0
Payload
60kg kg
10kg
100 kg
80
k5
Maximum speed
1 m/s
m/s
1.5 m/s
I 1m/s
mm/s
I 1m/s
Runtime
2-5h
4h
7-8h
3h
9h
8-10h
Cost
$5.4k
$25k
$7.6k
$13k
$20k
$100k
$100k
out needing to set up new circuitry for power supply and voltage conversion. Additionally, we open source the entire control stack, all the way down low-level control with motor velocity commands. This means researchers can have full control and not limited to the API functionality exposed by manufacturer's 's proprietary software stack.
Reliable and easily-sourced parts. One challenge with building a mobile robot for research is the sourcing of parts such as motors, encoders, gears, and custom machined parts, which can be time consuming and costly. We design our drive system mainly using parts sourced from suppliers of the FIRST robotics competition (FRC) [56] for high schoolers. These are the same parts used by over 80,000 competition participants each year. Due the popularity of the competition, the components are standardized and readily available for purchase from online vendors, and can be ordered online and typically delivered within a week. These parts are very reliable, they have been battle-tested in the strenuous conditions of competition (125 lb robots moving at high speeds with frequent collisions), and core software components such as CAN drivers, motor control, and battery monitoring are all included. Our caster module design contains only three easily-obtained custom parts as described in Sec. 3.1. The top and bottom plates the frame are laser-cut acrylic, which can be made with laser cutter or directly ordered using an online service. All other components of the mobile base be readily purchased from online retailers such Amazon.
Easy assembly and repair. Our holonomic mobile base design is easy assemble and can be put together in 1 to 2 days. Putting together the T-slot extrusion frame is the most time-consuming part of the process and takes around 6 hours. Each of the four caster modules can be assembled in less than 30 minutes using only hand tools, and subsequently installed on the frame. The custom wheel mounts for the casters can be 3D printed across days. All electrical wiring can be done in less than 30 minutes and requires no soldering. For repairs, all parts can easily removed from the frame modular fashion, including the caster modules. The robot does not need to be shipped back to manufacturer when parts break. Instead, replacement parts be purchased online and directly swapped out for the broken ones.
3.3 Specifications
In Tab. 1, we compare our holonomic base with other mobile bases and mobile manipulators com- monly used in research. This includes the Stretch mobile manipulator from Hello Robot, the Tracer and Ranger Mini 2.0 AGVs from AgileX, the Husky AGV from Clearpath, and the Fetch and Tiago mobile manipulators. Our mobile base is maximally maneuverable, performant, and flexible, while also having the lowest cost. Note that while the Tiago is holonomic, it uses mecanum wheels which make the robot vibrate as it moves.
Dimensions. Our robot has a small footprint (54 x 50 cm), enabling it to navigate in household environments, including narrow doorways and hallways. The top of the mobile base is approxi- mately 37.5 em above the ground. This height allows the mounted arm comfortably reach down towards the ground and up towards tabletops and countertops. Note that these dimensions can be casily customized to accommodate other arms.

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Weight. The mobile base weighs 1b (34 kg). We mount Kinova Gen3 7-DoF arm on top that weighs 27 lb k1) including its mounting plate and power supply unit.
Payload. To get a rough idea of the robot's maximum payload, we loaded 270 (122 kg) of weight plates onto the mobile base, for a total weight of 345 (156kg). Even with this amount of weight, found that the motors showed signs of struggle. For a conservative estimate of maximum payload suitable for long-term use, we halve the weight and use 60kg kg as our estimate. This indicates that our design can comfortably support the weight of many other arms, as long as the frame is redesigned appropriately for securely mounting the arm. Note also that the payload may vary depending on the terrain (we conducted our test on a hard floor).
Runtime. We power the robot arm and compute using portable power station with 768 Wh of capacity, which we found can handle 8 hours of continuous teleoperation runtime. For the motors, since they boot nearly instantly upon connection to power, the SLA battery powering the motors can be easily hotswapped during use if the voltage gets low.
Traversability. Our mobile base works on various indoor floor surfaces ranging from hard floor to high pile carpet, and can traverse many common floor obstacles such as door thresholds, floor mats, and elevator gaps. While not intended for outdoor use, we found during transport of our robot that it can handle many outdoor floor obstacles as well, such as bumpy sidewalks, steel construction plates, loading ramps (inclination 6.5 degrees), and speed bumps.
Odometry. To evaluate the odometry accuracy of the mobile base, we use motion capture with submillimeter accuracy to measure the pose of the base while driving it several path shapes. Overall, we find the odometry quality to be quite high, with translation drift of less than cm per meter of distance traveled, and rotation drift of less than 1 deg per 360 deg of rotation. This means that base movements are highly repeatable, mode to accurately reach goal poses (z,, 8) using odometry feedback.
our holonomic base can be controlled in position
4 Experiments
In these experiments, we aim to show (i) that our teleoperation interface can collect useful demon- stration data to successfully train policies for variety of household mobile manipulation tasks, and (ii) that holonomic drive offers advantages over differential drive for both teleoperation and policy learning. For qualitative videos of autonomous policy rollouts and telcoperation, please see our project page at http://tidybota/ htt.hybott..tttt....... github.io..
4.1 Imitation learning
We used phone teleoperation interface collect demonstrations for the 6 tasks shown in Tab. 2. We col- lected 100 demonstrations for the shorter open fridge task and 50 for all others. Data collection for each task took between 1 and hours for 50 episodes, including over- head for environment resets.
Table 2: Imitation learning results
Task
Success rate
Open fridge Wipe countertop
10/10
9/10
Load dishwasher
7/10
Take out trash
10/10
Load laundry Water plant
7/10
We then used the data to train a diffusion policy [1] for each task. We trained each policy for 500 epochs and evaluated them by running 10 episodes of policy rollouts.
6/10
Success rates are shown in Tab. 2. Note that while diffusion policies are typically trained using 200 to 300 demonstrations, we found that 50 was already sufficient for the robot to learn to complete the task successfully. Performance can likely be further improved with more data. These results show that our system is capable of learning nigh-performing policies for useful tasks in real homes.
4.2 Differential drive comparison
To further highlight the advantages of holonomic drive nonholonomic, conduct a head-to- head comparison the wipe countertop task. We collect 50 demonstrations of the task with our base
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operating in differential drive mode. We implement this by applying the appropriate nonholonomic constraints to the desired base pose and then computing a velocity command to send to the base.
Fig. 5 shows a representative pair of paths from the demonstration data (path begins the ori- gin), illustrating the greater efficiency enabled by holonomic base when compared to differ- ential drive. Across all demonstrations for this task, the differential drive base travels on av- erage 4.03 m (average episode duration 65.2s), whereas the holonomic base travels only 2.03 m (average episode duration 27.4 s).
Holonomic Differential drive
-02
-0.4
-0.6
(m)
-0.8
We then train a diffusion policy using the dif- ferential drive data and compare the resulting policy with the holonomic one from before. ensure fair comparison, the differential drive policy was trained under identical conditions (50 demonstrations and 500 epochs). While the holonomic base can perform the task with 9/10 success rate, we find that the differential drive policy only achieves 4/10 success rate.
1.0
-1.
-1.4
0.0
0.2
0.4
0.6 x (m)
0.8
1.0
1.2
Figure 5: In the wipe countertop task, the differ- ential drive robot is forced to take less efficient path as it is subject to nonholonomic constraints.
Qualitatively, we find that the differential drive policy does not learn the task as effectively, though was given the same number of demonstrations. The main failure mode is that it frequently skips portions of the countertop rather than wiping. We believe this is because the learning problem is strictly harder in the case of differential drive. The policy not only has to learn the wiping action, also has to learn a com- plicated sideways moving strategy similar to parallel parking, whereas the holonomic policy can directly move the base sideways. Additionally, the differential drive maneuvers cause the camera's view to swerve from side side, reducing its quality, while the holonomic base can maintain stable, facing camera view.
5 Limitations
One limitation of our mobile base that it does not backdrive very well due high steering friction in the caster modules, which can be attributed to the combination of high steer gear ratio (12.8) and small caster offset (14 mm). We confirmed that with the steer gearing removed, the base can indeed backdrive smoothly. The base could be made more easily backdrivable with further mechan- ical modifications using custom machined parts, but this would come at the cost of reducing the accessibility the open-source design.
Conclusion
In this paper, we proposed an open-source design for a mobile manipulator with an inexpensive, robust, and flexible holonomic base. The design uses powered-caster wheel modules, which makes the base holonomic and therefore With our easy-to-use mobile phone teleoperation interface, showed that many household mobile manipulation tasks become easy to demonstrate on our robot. Additionally, we showed that the data collected with our interface can be used to train high-performing policies on a variety of tasks. We hope that the open-source release of this mobile base design and teleoperation interface will enable the robot learning community to easily collect large quantities of mobile manipulation data that will form the basis for policy learning.
maneuverable than commonly used nonholonomic designs.

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Acknowledgments
We would like thank Kevin Zakka, Yixuan Huang, Kevin Lin, Zi-ang Cao, Jingyun Yang. Rika Antonova, Marion Lepert, Sophie Lueth, Haoyu Xiong, Huy Ha, Cheng Chi, Philipp Wu, Fred Shentu, and Zhongke Yi for fruitful technical discussions. We thank Rajat Kumar Jenamani, Rishabh Madan, and the Cornell EmPRISE Lab for open sourcing their compliant controller for the Kinova arm. We would also like to thank Zi-ang Cao, Rika Antonova, and Haoyu Xiong for extensive hard- ware assistance, as well as Rika Antonova and Zipeng Fu for lending hardware. We are especially grateful towards the FIRST Robotics Competition (FRC) community for developing the rich ecosys- tem that made this project feasible. For extensive product and logistics support, we would like to thank Mandy Gove, Cory Ness, Omar Zrien, Jacob Caporuscio, and Dalton Smith from CTR Elec- tronics; Ranjit Chahal and Harvey Rico from WestCoast Products; and John Rigsby from Swerve Drive Specialties. The work was supported in part by the Stanford Institute for Human-Centered Ar- tificial Intelligence (HAI), Princeton School of Engincering, the Sloan Foundation, and the National Science Foundation under 2143-1.4
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13


Reviews:
[{"review_id": "0LgOoIw6Jg", "reviewer": "Reviewer_rKYA", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 4, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "The paper introduces a open sourced holonomic base for household mobile manipulation, which is a missing part in robot learning community. The author provided a hardware design and teleop system design.", "summary_of_recommendation": "I will consider to raise my scores if I see more learning results.", "technical_quality": 4, "clarity_of_presentation": 4, "potential_impact": 4, "robotics_focus": 4, "quality_of_limitations": 2, "questions_for_rebuttal": "I am particularly interested in the following analyses from the author:\n\nHardware Analysis:\n\n1. Why holonomic? Discuss the advantages of using a holonomic base for mobile manipulation.\n2. What are the height limitations of the robot, and how does this affect stability? Are there stability tests, such as tipping over when force is applied?\n\nSoftware Toolbox:\n\n1. An open-source teleoperation toolkit.\n2. Basic simulation environment and URDF files.\n\nI will consider to raise my scores if I see more learning results:\n\n1. Basic ACT/ diffusion policy results with the tasks you picked in the paper.\n2. Define the initial distribution for task settings. Success rate metrics. Benchmark tasks for evaluation.\n\nIt would be beneficial if the paper discussed:\n\n1. The use of imitation learning for mobile manipulation: Are local or global actions more effective?\n2. The choice of sensory input: Is RGB or point cloud data preferable for mobile manipulation tasks?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lyF3xiLjgN", "reviewer": "Reviewer_NmwR", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "This paper proposes an open source low-cost mobile base built using off-the-shelf parts. The base is a powered caster design and holonomic. There is also an open source mobile app for teleoperation which uses the touchscreen and IMU of the phone. The authors show impressive teleoperation results with their base.", "summary_of_recommendation": "I am voting weak reject because of lack of demonstrated downstream application + some missing analysis (see questions). Happy to raise my score if concerns are addressed.", "technical_quality": 3, "clarity_of_presentation": 5, "potential_impact": 3, "robotics_focus": 4, "quality_of_limitations": 3, "questions_for_rebuttal": "- What is the action space for the base? Is it $(v_x, v_y, \\omega)$?. For a given action command, how does the behavior of the base change for different positions of the caster wheels? For eg. does the base hit singularities for some positions of the caster wheels? How does the imitation learning policy deal with this?\n- Similarly, to the above question, if the action space is velocity, how does the robot accelerate / decelerate to the commanded velocity? Does the exact profile depend on the wheel configuration?\n- What is the quality of the odometry data from the base?\n- Have the authors considered providing a simulation package for the base with accurate dynamics? This could be useful for researchers exploring algorithms in simulation or for sim2real.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oGgpvjeTq0", "reviewer": "Reviewer_j6ug", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 2, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "The paper contributes an open-sourced mobile platform for robot learning research. The two key features of the system are low cost and holonomic, which enables easier and faster task completion.", "summary_of_recommendation": "Great contribution but need more experiments. I am willing to raise my score conditioned on more experimental results during the rebuttal.", "technical_quality": 2, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 4, "quality_of_limitations": 3, "questions_for_rebuttal": "How does the proposed system compared to Reachy from Pollen Robotics?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The reviewers found some strengths in this submission, but also clearly articulated some questions for the rebuttal phase. 
Thank you for your responses.
Decision: Accept