DOGlove: Dexterous Manipulation with a Low-Cost
Open-Source Haptic Force Feedback Glove
Han Zhang1 , Songbo Hu1 , Zhecheng Yuan1,2,3 , Huazhe Xu1,2,3
1
Tsinghua University, 2 Shanghai Qi Zhi Institute, 3 Shanghai AI Lab
https://do-glove.github.io/
Motion Capture Haptic Force Feedback
21 DoFs 5 DoFs
arXiv:2502.07730v1 [cs.RO] 11 Feb 2025
DOGlove Shadow Hand LEAP Hand
a) Teleoperation without Visual Feedback b) Regulating the Sauce Flow c) In-Hand Rotation with Force-Based Extrinsic Dexterity
Fig. 1: DOGlove, a haptic force feedback glove designed for precise teleoperation and dexterous manipulation. It features 21-DoF motion capture and 5-DoF
haptic force feedback. By leveraging action and force retargeting, it enables the teleoperation of dexterous hands for complex, contact-rich tasks, including:
a) without visual feedback, adjusting contact force with a bottle during teleoperation, b) regulating the flow of condensed milk, and c) performing in-hand
rotation by using haptic force feedback to adjust friction.
Abstract—Dexterous hand teleoperation plays a pivotal role I. I NTRODUCTION
in enabling robots to achieve human-level manipulation dex-
Imitation learning (IL) has shown significant promise in ad-
terity. However, current teleoperation systems often rely on
expensive equipment and lack multi-modal sensory feedback, dressing complex manipulation tasks [7, 8, 46, 45]. However,
restricting human operators’ ability to perceive object properties it often necessitates a substantial amount of task-specific data
and perform complex manipulation tasks. To address these to train a generalizable learning policy. Efficiently collecting
limitations, we present DOGlove, a low-cost, precise, and haptic and ensuring the high quality of such demonstrations remains a
force feedback glove system for teleoperation and manipulation.
persistent and challenging problem for the robotic community.
DOGlove can be assembled in hours at a cost under 600 USD.
It features a customized joint structure for 21-DoF motion Teleoperation is among the most commonly used methods
capture, a compact cable-driven torque transmission mechanism for collecting demonstrations, often involving the development
for 5-DoF multidirectional force feedback, and a linear resonate of a wide range of devices tailored to meet diverse data
actuator for 5-DoF fingertip haptic feedback. Leveraging action acquisition requirements. These devices enable the transfer
and haptic force retargeting, DOGlove enables precise and im-
of human manipulation behaviors to various robotic plat-
mersive teleoperation of dexterous robotic hands, achieving high
success rates in complex, contact-rich tasks. We further evaluate forms [13, 12, 6, 10, 15]. However, when it comes to dexterous
DOGlove in scenarios without visual feedback, demonstrating hands, their high degrees of freedom (DoFs) and inherent
the critical role of haptic force feedback in task performance. In complexity impose even stricter demands on operational pre-
addition, we utilize the collected demonstrations to train imitation cision and the accuracy of human motion capture. Hence, it is
learning policies, highlighting the potential and effectiveness of
crucial to design an intuitive, responsive, and highly precise
DOGlove. DOGlove’s hardware and software system will be fully
open-sourced at https://do-glove.github.io/. device specifically suited for dexterous hand teleoperation
applications.
a) Teleoperate the LEAP Hand to squeeze condensed milk onto the bread
b) Grasp the bottle without relying on visual feedback c) Identify objects without visual feedback
Fig. 2: Teleoperation demos. a) While squeezing condensed milk, the operator regulates the flow using haptic force feedback from DOGlove. b) The operator
grasps a slipping bottle without visual feedback. c) The user identifies object pairs solely through haptic force feedback.
Vision-based methods are primarily used for tracking the integration of force and haptic feedback creates an immersive
human hand in dexterous hand teleoperation. A simple ap- and responsive interface for dexterous manipulation.
proach involves using RGB cameras [25, 35], but the accuracy Action and haptic force retargeting: We propose a general
of hand gesture capture is often questioned and may be further retargeting framework. For action retargeting, the rigid con-
limited by visual obstacles during hand-object interactions. straints of the glove allow fingertip positions to be mapped
Motion capture (MoCap) systems [23, 36, 27, 38] provide sta- from the human hand to the target robotic hand. For haptic
ble hand tracking. However, relying solely on visual feedback force retargeting, the combination strategy enables users to
for teleoperation makes intuitive control challenging for the perceive contact information during teleoperation.
human operator. The resulting system, DOGlove, provides precise hand pose
Haptic force feedback offers additional perceptual charac- motion capture and the ability to sense interactions with ma-
teristics beyond those provided by vision, such as the ability nipulated objects. This enables human operators to intuitively
to sense an object’s weight, friction, and softness. Integrating and efficiently teleoperate dexterous hands. As shown in Fig. 2,
haptic feedback into teleoperation can enrich the feedback it further supports the completion of complex manipulation
available during interaction and enable the completion of tasks. We evaluate the necessity of haptic force feedback
more challenging tasks. Recently, some commercialized force through a user study and further assess the teleoperation effi-
feedback gloves [9, 29, 21, 14] have shown promise for ciency and data accuracy of DOGlove in several quantitative
enabling intuitive teleoperation. However, these solutions are experiments.
often prohibitively expensive and require significant integra- Finally, we demonstrate that DOGlove seamlessly integrates
tion efforts to work with existing robot learning frameworks. with existing methods in robot learning. We use DOGlove to
In this paper, we introduce DOGlove, a low-cost, fully teleoperate the LEAP Hand mounted on a Franka robot arm,
open-sourced, and easy-to-manufacture haptic force feedback collecting data to train imitation learning policies. To foster
glove for dexterous manipulation. The glove can be assembled further research, we will open-source the mechanical designs,
in hours for a total cost of 600 USD. Key features of DOGlove circuit designs, embedded code, assembly instructions, URDF
include: models, retargeting methods, and MuJoCo simulation environ-
21-DoF motion capture: DOGlove features an anthropo- ment at https://do-glove.github.io/.
morphic design resembling the human hand, providing precise
II. R ELATED W ORK
motion capture and a comfortable wearing experience. More-
over, we propose a customized joint structure that integrates a A. Data Collection from Human Demonstrations
compact, low-cost yet accurate joint encoder, with the entire A substantial amount of task-specific data is essential for
assembly measuring less than 15 mm in thickness. imitation learning. In dexterous manipulation, obtaining high-
5-DoF haptic force feedback: DOGlove leverages a cable- quality hand motion data is critical for training effective poli-
driven mechanism to deliver force feedback to each finger cies. Prior work includes extracting demonstration data from
while maintaining a compact and cost-effective design. Addi- human videos [32, 40, 41, 2] and hand trajectories [37, 42].
tionally, each fingertip is also equipped with a linear resonant While these approaches are accessible and have shown promis-
actuator (LRA) to provide realistic haptic feedback. This ing results, the significant visual gap between recorded human
demonstrations and the robot’s perception often makes real- While ensuring these functionalities, the glove is optimized for
world transfer challenging. An alternative is using dedicated accessibility by the research community, focusing on low cost,
hardware for data collection to bridge this gap. Hand-held ease of manufacturing, and high performance. To achieve these
grippers [33, 8, 26] have proven effective in capturing robot goals, DOGlove incorporates the following design principles:
manipulation data. However, these systems are primarily de-
signed for parallel grippers. Another widely used approach A. Low cost
is MoCap systems, which record human demonstrations and Commercial products such as the SenseGlove Nova [29]
extract hand motion data. These systems include camera-based and Manus VR [21] cost more than 5,000 USD, making
methods [25, 49], glove-based tracking systems [38, 19, 20], them prohibitively expensive for many researchers. In contrast,
marker-based tracking [48], and commercial MoCap solu- DOGlove provides a low-cost solution under 600 USD.
tions [34, 11]. While MoCap offers high-precision tracking,
B. Ease of manufacturing
bridging the embodiment gap between human and robotic
hands remains a persistent challenge. All parts of DOGlove are either readily available for pur-
chase online or manufacturable using standard methods. The
B. Dexterous Hand Teleoperation glove’s main body can be 3D-printed using a commodity 3D
Collecting high-quality human demonstrations through printer, while the remaining electronics and servos are easily
robotic teleoperation systems [12, 6, 10, 15] also plays a sourced. The entire glove can be assembled within 6 hours.
critical role for advancing dexterous manipulation. Existing
C. Performance Sufficiency
research has explored teleoperation from various perspectives,
including leader-follower setups such as ALOHA [46, 47, To ensure precise fingertip position tracking, the glove’s
13, 1]. However, teleoperating dexterous hands remains a encoders deliver joint angle data with an error range of ±7.2°,
significant challenge. OpenTelevision [6] leverages VR devices which can be further minimized through careful calibration.
to capture hand poses and streams the pose information For intuitive haptic force feedback, the servos provide suffi-
for retargeting to robotic hands. BiDex [31], on the other cient stall torque to halt human finger movement, while the
hand, implements a teleoperation system based on commercial haptic engine supports multiple haptic waveforms to enhance
motion capture gloves [21] and leader arms. Compared to these tactile sensations.
frameworks and other glove-based systems [19, 20], DOGlove
D. Low latency
offers distinct advantages. It eliminates the need for expensive
equipment while precisely capturing fingertip positions and The MoCap system operates at a maximum frequency of
delivering richer haptic force feedback to the operator. This 120 Hz, while the haptic force feedback system achieves a
system achieves accurate dexterous hand teleoperation with a maximum frequency of 30 Hz. Together with the retargeting
low-cost setup, making it an efficient alternative. algorithm, the system ensures seamless operation at a mini-
mum frequency of 30 Hz, providing a smooth and responsive
C. Teleoperation with Haptic Force Feedback teleoperation experience.
While recent studies rely on visual information to capture IV. H ARDWARE S YSTEM
environmental characteristics, vision alone inherently limits
the richness of available sensory data. In contrast, haptic force A. Kinematic Design
feedback enhances the teleoperation experience by providing The kinematic design of DOGlove refers to the use of con-
greater immersion and improving perception of the robot’s straints to achieve desired movements, emulating the natural
status and movement compared to vision-based methods. motion of a human hand. To ensure precise MoCap capabilities
Bunny-VisionPro [10] and Liu et al. [20] apply real-time haptic and a comfortable wearing experience, DOGlove are designed
feedback to enable more accurate manipulation. Xu et al. [39] to closely resemble the anthropomorphic structure of the
build a bilateral isomorphic bimanual telerobotic system using human hand.
a commercial force feedback glove [9] to enhance percep- Several studies [3, 4] model the hand skeleton as a kine-
tion and improve performance in complex tasks. NimbRo- matic chain, represented by a hierarchical structure of rigidly
Avatar [28] and Mosbach et al. [22] integrate commercial connected joints. As shown in Figure 3, the kinematic structure
force feedback glove [29] into dexterous teleoperation systems. of the human hand primarily consists of two types of joints:
However, these approaches rely on specialized or expensive hinge joints and ball joints.
equipment. In contrast, DOGlove provides a highly accurate In the index, middle, ring, and pinky finger, distal inter-
teleoperation system with integrated haptic force feedback at phalangeal (DIP) and proximal interphalangeal (PIP) joints
a significantly lower cost and can be widely used in dexterous are hinge joints with 1-DoF, allowing only flexion-extension
manipulation. movements. In contrast, the metacarpophalangeal (MCP) joint
is a ball joint with 2-DoF, allowing both flexion-extension and
III. G LOVE D ESIGN O BJECTIVES adduction-abduction movements.
DOGlove is designed to precisely capture human hand poses The thumb differs slightly in its structure. The interpha-
and provide haptic force feedback for intuitive teleoperation. langeal (IP) and metaphalangeal (MCP) joints are hinge joints
Ring Pinky Ring Pinky
Middle Middle
Index
Index
PIP
DIP
DIP
PIP
MCP_B
MCP (B+S)
MCP_S
Thumb
Thumb Wrist_R
IP
Wrist
MCP MCP
IP TM (B+S)
TM_B
B = Bend (flexion-extension)
TM_S S = Split (adduction-abduction)
R = Rotate (pronation–supination)
Fig. 3: The kinematic structure of DOGlove, designed to replicate the kinematics of a human hand. The MCP (B+S) and TM (B+S) joints are modeled as
ball joints using a combination of two rotary joints. The right figure from [3] illustrates the simplified human hand kinematics.
with 1 DoF, while the additional trapeziometacarpal (TM)
joint is a ball joint that supports both flexion-extension and
adduction-abduction movements. To further enhance dexterity,
an additional DoF at the wrist allows the thumb to perform
pronation-supination movements.
To implement these joints in DOGlove, hinge joints are
modeled as two linkages connected by a rotary joint, with
a joint encoder installed on the rotary axis to capture motion.
Ball joints are designed as a combination of two orthogonal
rotary joints, each equipped with a joint encoder on its
respective rotary axis.
The linkage lengths in DOGlove are designed to accommo- Proper linkage lengths
date the majority of adult human sizes. To achieve this, a stan-
dard human finger length was first modeled, and the glove’s
linkage parameters were simulated to ensure an optimal range
of motion. As shown in Figure 4, improper linkage lengths can
obstruct the natural flexion-extension of the fingers, leading
to discomfort and reduced MoCap performance. Furthermore,
DOGlove features a modular design where all fingers share a
common structural framework. This modularity enables users
to replace linkages with customized sizes as needed, enhancing
Improper linkage lengths
both adaptability and usability.
Fig. 4: Improper linkage lengths can cause collisions between the human
B. Finger Design finger (link f, g) and the glove (link m), restricting finger movements and
As shown in the exploded-view in Figure 3, DOGlove is leading to discomfort and poor MoCap performance.
composed of the thumb, index, middle, ring, and pinky finger
assemblies, along with the palm base structure. The design of The exploded view of a single finger is illustrated in
each finger assembly follows a modular approach, ensuring Figure 5. The highlighted area indicates the basic components
consistent structural elements across all fingers. of a rotary joint. Each rotary joint is constructed using an
Fixed Pulley B
M3 Locknut
Fixed Pin
M3*20 Headless Clevis Pin
Joint Encoder
with Retaining Ring Groove
Fixed Pulley A
Servo Pulley
Ball Bearing
M4*15 Shoulder Screw
M4*15 Shoulder Screw
Joint Holder
Finger Proximal Finger Pulley B
Finger Distal Finger Middle
Dynamixel XC/XL330
Finger Pulley A
LRA M4*20 Headless Clevis Pin Motor Shaft
with Retaining Ring Groove
Ball Bearing
LRA Holder
Fig. 5: Exploded view of the finger assembly, with the highlighted area indicating the basic components of a rotary joint.
M4×15 shoulder screw to connect the finger linkages, ball to the actual joint angle. These voltage signals are read
bearing, and joint encoder, secured with an M3 locknut. This by an Analog-Digital Converter (ADC) module. For precise
design ensures smooth and reliable joint rotation. The main conversion, we use the TI ADS1256, a low-noise 24-bit
body of the finger, colored white and gold, is 3D printed using ADC operating at 30k samples per second. The converted
PETG material for ease of fabrication and durability. signals are then sent to a microcontroller unit (MCU), the
Given the limited space on the back of the human hand, ST Electronics STM32F042K6T6, which operates at a
the finger assembly’s width is constrained to less than 26 clock speed of 48 MHz. To optimize system performance and
mm. Simultaneously, to provide effective force feedback, the reduce OS scheduling overhead, the STM32’s Direct Memory
actuator must deliver a stall torque of at least 0.5 N·m. Access (DMA) feature is utilized to accelerate joint encoder
Additionally, adjustable stiffness requires the actuator’s current readings. Finally, the processed joint data is transmitted to the
to be regulated. Since the actuator is directly connected to host machine via a serial port on the STM32.
the pulley system as a rotary joint for M CPB , it is essential The voltage readings are mapped directly to joint an-
to measure its rotary position in real time to achieve precise gles under the assumption that the supply voltage of the
joint angle control. Considering these design requirements, the STM32 (approximately 3.3 V) corresponds to 360°, while the
Dynamixel XC/XL330 servo motors were selected as the ground voltage (0 V) corresponds to 0°. Using the ADC output
actuators for force feedback. It fulfills the torque, size, and voltage, the joint angle is calculated as:
real-time position measurement needs, making it a suitable
VADC
choice for DOGlove. αjoint = · 360 (1)
VCC
- Joint Encoders:
The primary error in this conversion comes from the lin-
To integrate joint encoders into the finger linkages, the
earity error of the encoder which is ±2% according to its
encoders must be compact while maintaining high precision.
datasheet. This results in an angular error of ±7.2° when mea-
Additionally, as 16 encoders are required in combination with
suring joint angles. To mitigate this, we employ a calibration
5 servo motors to achieve 21-DoF MoCap capabilities, the
process. Using an external high-precision joint encoder, we
cost of each encoder needs to be affordable. Considering
map the voltage reading to an actual joint angle, creating a
these constraints, we selected the Alps RDC506018A rotary
correction table for each encoder. With this calibration, the
sensor as the joint encoder. This compact encoder (W11 mm
error can be reduced to within ±1°.
× L14.9 mm × H2.2 mm) is easily integrated into the 3D-
printed joint structures. The encoder operates as a variable 2) Cable-Driven Force Feedback Structure:
resistor, changing its resistance as the shaft rotates. To provide force feedback on the human fingers, the output
The resistance changes are converted into voltage sig- torque of the Dynamixel servo must be transmitted to the
nals using a simple voltage divider circuit. Due to the en- glove’s finger linkage system. As illustrated in Figure 5, the
coder’s linear response, the voltage output is proportional rotary axis of the servo and the rotary axis of the M CPB
joint are misaligned. Consequently, a transmission mechanism fingertip in DOGlove is equipped with a tactile actuator.
is required to transfer the torque effectively. Traditional haptic actuators include eccentric rotating
Although a bevel gear system could serve as a potential mass (ERM) motors and linear resonate actuators (LRAs).
solution, its implementation would require significant space to Limited by the inertia of the rotating mass, ERM motors
accommodate the two orthogonal gears. Additionally, transmit- are slow to start and stop, making it challenging to produce
ting large torque through gears can cause deformation in the complex waveforms needed for subtle tactile sensations. On
gear shaft, leading to gear slippage. In contrast, a cable-driven the contrary, LRAs offer linear motion, resulting in a cleaner
mechanism offers a more compact design while ensuring stable and more precise tactile output.
torque transmission. In DOGlove, we use LRAs with a diameter of 8 mm and
Traditional cable-driven systems typically provide unidi- a height of 2.5 mm, installed close to the fingertips. These
rectional force transmission on the tension side, relying on LRAs provide vibration stimuli by resonating at approximately
a spring to generate force in the opposite direction. How- 240 Hz along Z axis, which is orthogonal to the fingertip
ever, this approach introduces unrealistic feedback sensations. surface. Operating at 1.2 Vrms , the LRAs generate high-
While using two servos per finger could resolve this issue, it quality haptic waveforms. To fully leverage the potential of
would significantly increase the glove’s weight and cost. the LRA, we employ the TI DRV2605L motor driver, which
includes the licensed Immersion TouchSense© 2200
Servo Pulley haptic library. This driver supports over 100 pre-programmed
waveforms, allowing DOGlove to deliver realistic and refined
haptic feedback.
Fixed Pulley B
C. Wrist Localization
In DOGlove, we design a shell with a 1/4 inch screw con-
nector to accommodate external wrist localization devices. For
Cable B Fixed Pulley A
our experiments, we use the HTC Vive Tracker for real-
Finger Pulley B time wrist position tracking. However, the design is compatible
Cable A with other solutions, depending on the user’s requirement.
V. R ETARGETING
A. Action Retargeting
To map human hand gestures to a robotic hand, it is essential
to perform action retargeting, which converts motion data
Finger Pulley A from the glove into robotic hand movements. This process
addresses both the embodiment gap and motion discrepancies.
Fig. 6: Pulley system of the cable-driven mechanism. Previous studies [38, 11, 34] highlight the significance of
fingertips, as they are the primary contact area during object
To address these challenges, DOGlove utilizes a pulley
interactions. Building on this insight, we apply the 5-DoF
system to provide the bi-directional force feedback, as shown
haptic force feedback to the human operators’ fingertips and
in Figure 6. DOGlove uses a 0.6 mm stainless steel braided
adopt a retargeting method focused on fingertip positions.
wire as the cable, chosen for its strength and durability. The
Our approach combines Forward Kinematics (FK)
Servo Pulley connects the servo to the finger linkage (Finger
to compute human fingertip positions and Inverse
Middle) via the Finger Pulley, maintaining a 1:1 transmission
Kinematics (IK) to calculate the corresponding robotic
ratio. To minimize friction during transmission, the Fixed
hand positions. When wearing DOGlove, the human operator
Pulley is used to redirect the cable’s path. When the Servo
secures their fingertips inside the finger caps. Since the glove
Pulley rotates clockwise, the tension on Cable B increases,
acts as a rigid body, the relative positions of the fingertips with
causing Finger Pulley to rotate clockwise. The extra slack
respect to the glove’s origin can be accurately calculated. With
on the Cable A side is taken up by the Servo Pulley A.
DOGlove’s anthropomorphic kinematic design and precise
Since the finger linkage is fixed to the Finger Pulley, it also
MoCap capabilities, fingertip positions are effortlessly deter-
rotates clockwise, resulting in the extension movement of
mined using the glove’s built-in FK. To map these positions
the M CPB joint. Similarly, when the Servo Pulley rotates
to a robotic hand, we utilize Mink [44], a differential inverse
counterclockwise, the tension shifts to Cable A, producing a
kinematics library, to generate smooth and feasible motions
flexion movement of the M CPB joint.
for the robotic hand.
This configuration enables bi-directional torque transmis-
A size discrepancy often exists between the human hand and
sion while maintaining a simple, compact, and cost-effective
the target robotic hand. To address this, we introduce a scaling
design.
factor when calculating IK, allowing adaptation to different - Fingertip Haptic Feedback: robotic hand sizes. This ensures an intuitive teleoperation
To further enhance the operator’s tactile experience, each experience where the robotic hand naturally mirrors the human
LEAP Hand Shadow Hand Inspire Hand Allegro Hand
Fig. 7: Action retargeting results: Teleoperating the LEAP Hand to grasp a toy in the real world and teleoperating the Shadow Hand, Inspire Hand, and
Allegro Hand in simulation.
hand’s gestures. For instance, when the human operator opens from noise, we set the first threshold at 10 g to initiate
their hand, the robotic hand open proportionally. Similarly, haptic feedback. During a user study without visual feedback
when the human operator brings their thumb and index finger (Section VI-A), we observe that human operators are highly
together, the robotic hand’s thumb and index finger also touch. sensitive to force feedback. To create a more realistic expe-
This ability for precise fingertip alignment is critical for tasks rience, force feedback is applied only after the force sensor
like grasping small objects. readings exceed 50 g, which serves as the second threshold.
In our experiments, we deploy the system on the LEAP Furthermore, observations from the bottle-slipping experiment
hand [30] in real-world scenarios and test it with various (Section VI-B) reveal that continuous haptic feedback during
robotic hands in the MuJoCo simulator. Figure 7 presents the teleoperation can create a misleading sensation, interfering
retargeting results of DOGlove in both simulation and real- with the operator’s ability to perceive the subtle properties
world environments. of the object’s surface. To address this, a third threshold is
set at 100 g, where haptic feedback stops, leaving only force
B. Haptic Force Retargeting
feedback active.
To provide the haptic force feedback, it is first necessary For force feedback, the Dynamixel servos operate
to sense tactile or force information at the robotic hand’s in current-based position control mode. The
fingertips. This can be achieved using a simple tactile sensor, force readings from the LEAP Hand fingertips are clamped
such as the force sensing resistor (FSR) sensor [10, 16], or to the range [0g, 3000g], and mapped linearly to the KP
for better performance, by utilizing an F/T sensor [17, 5] or a gain of the Dynamixel servos. For haptic feedback, we use
vision-based tactile sensor [43, 18]. waveform ID 56 from the haptic engine library, corre-
In our experimental setup, we install a 1-D force
sponding to Pulsing Sharp 1-100%.
sensor on each fingertip of the LEAP Hand, with a mea-
This combination strategy for haptic force retargeting en-
surement range of 3 kg and a precision of 1 g. During our
ables human operators to distinguish object shape, size and
quantitative experiments (Section VI), we identify a com-
softness without visual feedback. It also improves performance
bination strategy for integrating haptic and force feedback
in complex, contact-rich manipulation tasks. Further details are
that optimizes performance. This strategy along with the
provided in Section VI.
corresponding thresholds and feedback patterns is summarized
in Table I.
VI. E XPERIMENTS
Force Sensor Readings (g) Haptic Feedback Force Feedback
<10 ✗ ✗ In this section, we use DOGlove to teleoperate the LEAP
10–50 ✓ ✗ Hand [30] mounted on the Franka Robot Arm to evaluate
50–100 ✓ ✓ its effectiveness through a series of challenging tasks across
>100 ✗ ✓
three key aspects:
Table I: The combination strategy for haptic force feedback in DOGlove.
• Haptic Force Perception: Without visual feedback, how ef-
When the robotic hand touches an object, the force sen- fectively can DOGlove assist human operators in perceiving
sor readings increase. To effectively distinguish these signal object properties through haptic force feedback?
• Teleoperation Efficiency: Does integrating haptic force five object pairs, selected based on factors such as shape, size,
feedback improve vision-based teleoperation success rates and softness.
and reduce task completion time? Can DOGlove enable Metrics: Users’ ability to distinguish object pairs is evaluated
human operators to perform challenging, contact-rich ma- based on their success rate.
nipulation tasks? Challenges: The five object pairs are intentionally chosen
• IL Compatibility: Can the data collected via DOGlove be based on the following considerations:
leveraged to train IL policies for dexterous manipulation? • Pair 1: Basic Pair, different shape. The ball and the box have
Evaluation Setup: To evaluate the effectiveness of haptic distinctly different shapes (Fig 8b).
force perception, we conduct a user study (Section VI-A) and • Pair 2: Basic Pair, similar shape, different size. The peanut
a quantitative experiment (Section VI-B). Teleoperation effi- bottle and the coffee paper cup share a similar cylindrical
ciency is assessed in Experiment VI-C, while IL compatibility shape, but their diameters differ slightly (Fig 8c).
is evaluated in Experiment VI-D. • Pair 3: Basic Pair, similar softness, different size. The
two toys have similar softness and shapes but vary in
Comparisons: All experiments share the following compar- size (Fig 8d).
ison conditions, although a subset of these may be selected • Pair 4: Challenging Pair, similar size and shape, different
depending on the specific task setup: softness. Two identical bottles are used, one filled with pure
• Only Force: Force feedback is enabled only when the water (soft) and the other filled with carbonated cola, shaken
force sensor readings exceed 10 g. to increase its hardness (Fig 8e).
• Only Haptic: Haptic feedback is enabled only when the • Pair 5: Challenging Pair, similar shape, different size and
force sensor readings exceed 10 g. softness. A toy cabbage (softer, larger) and a real cab-
• Haptic+Force: A combined feedback strategy is applied, bage (Fig 8f).
as detailed in Section V-B.
• No Haptic/Force: DOGlove is used solely for MoCap, Pair 1 Pair 2 Pair 3 Pair 4 Pair 5
with no feedback provided.
Only Force 5/5 5/5 5/5 4/5 0/5
• Baseline: AnyTeleop [25], a widely recognized vision- Only Haptic 5/5 5/5 5/5 3/5 3/5
based hand retargeting method, is used as the MoCap Haptic+Force 5/5 5/5 5/5 3/5 2/5
baseline.
Table II: Success rates in the user study. All feedback modes perform well
for the basic pairs. For the challenging pairs, force feedback is more sensitive
A. User Study: Object Perception w/o Visual Feedback to softness, while haptic feedback is more sensitive to shape.
Performance: As shown in Table II, even without visual
and auditory feedback, all participants effortlessly distinguish
basic pairs 1-3. For challenging pair 4, most participants can
Earphones perceive softness using only force feedback. Some also discern
Eyemask d)
softness using only haptic feedback by evaluating the duration
of contact during deformation.
For challenge pair 5, when the robotic hand grasps the
softer toy cabbage, it deforms to resemble the size of the real
cabbage. This deformation increases its perceived softness,
a) e)
making it difficult for participants to distinguish using force
feedback alone.
For both challenge pairs, combining haptic and force feed-
back slightly reduces user sensitivity, leading to a marginally
lower accuracy.
b) c) f) B. Bottle-Slipping
Fig. 8: User Study. (a) Experiment setup: Users wear an eyemask and 1) Teleoperation w/o Visual Feedback
headphones to eliminate visual and auditory feedback. (b)–(f) Object pairs Task: In this experiment, the human operator must per-
tested in the study.
form a bottle-slipping action relying solely on feedback from
Task: Five untrained human operators participate in this user DOGlove. A 15-second countdown timer is set for each trial.
study. During the experiment, they are required to distinguish If the bottle successfully slips without falling within the 15
between five pairs of objects solely through feedback from seconds, the trial is denoted as successful.
DOGlove, without any visual or auditory input (achieved by Metrics: The success rate.
wearing an eyemask and headphones). In each trial, a pair of Challenges: Without any visual or auditory input (achieved
objects is randomly selected, and users provide their answers by wearing an eyemask and headphones), the operator must
immediately after experiencing feedback from DOGlove for determine if the bottle is slipping at the right speed or too
both objects. Figure 8 illustrates the experiment setup and the quickly, risking a fall.
w/o Visual with Visual
a) Bottle-Slipping Experiment
b) Rotating and Placing the Carton
Fig. 9: Teleoperation experiments and quantitative results. a) Without visual feedback, force feedback significantly improves the task success rate. With
visual feedback, it enhances precise control. b) In in-hand rotation, the challenge is to slightly release the fingers, allowing the carton to rotate without slipping
out (as shown in the middle two images).
Performance: As shown in Fig 9a, force feedback signifi- also teleoperation precision. To minimize slipping deviation,
cantly improves the success rate of this task. Additionally, operators are instructed to control the LEAP Hand carefully
incorporating haptic feedback further enhances overall perfor- and optimally. As shown in Fig 9a, similar to previous results,
mance. However, since the fingers of the LEAP Hand maintain haptic feedback does not provide additional information and
continuous contact with the bottle during the task, haptic may even interfere with task precision. However, force feed-
feedback does not provide additional information beyond using back enables operators to minimize slipping deviation more
the glove solely as a MoCap device, resulting in the same effectively. While using DOGlove solely as a MoCap device
success rate for both conditions. achieves the same success rate as with haptic force feedback,
Due to differences in retargeting strategies, even a slight it results in a larger average slipping deviation.
change in human finger position can lead to a significant
C. Rotating and Placing the Carton
deviation in the LEAP Hand’s movements. As a result,
AnyTeleop [25] struggles to perform the slipping task effec-
Success Rate Average Completion Time (s)
tively.
Only Force 9/10 18.92
2) Teleoperation with Visual Feedback Only Haptic 9/10 21.16
Task: Unlike the previous blindfolded experiment, this ex- Haptic+Force 10/10 19.89
No Haptic/Force 4/10 24.76
periment allows operators to have visual feedback. To further AnyTeleop 1/10 54.85
evaluate the operator’s control ability, they are required to slip
the bottle to a specified distance (9 cm). A trial is denoted Table III: Quantitative experiment results. Haptic force feedback enables
operators to achieve a higher success rate and a faster average completion
as successful if the bottle slips without falling. Additionally, time, as haptic feedback provides contact information, while force feedback
We measure the deviation between the actual slipping distance indicates the proper timing for in-hand rotation.
and the target distance (9 cm). Task: This is a long-horizon contact-rich task. As shown in
Metrics: Performance is evaluated using two metrics: Fig 9b, the operator must first pick up the carton horizontally,
• Success Rate: A trial is denoted as successful if the bottle then perform an in-hand rotation, orienting the carton verti-
slips without falling. cally before placing it into a small bucket.
• Slipping Deviation: This measures the difference between Metrics: Performance is evaluated using two metrics:
the target sliding distance (9 cm) and the actual slipping • Success Rate: A trial is denoted as successful if the carton
distance, with a smaller deviation indicating greater opera- rotates more than 45 degrees and is successfully placed into
tional accuracy. the bucket.
Challenges: Operators must precisely control the bottle to • Completion Time: The total time taken to complete the
achieve the desired distance. While a greedy approach often entire process.
causes the bottle to fall and results in failure, a conservative Challenges:
approach leads to an unsatisfactory distance deviation. • Precise Manipulation: The operator must accurately teleop-
Performance: This task evaluates not only success rate but erate to rotate the carton while preventing it from falling.
a) Press and Move Box
b) Pick and Place Teddy Bear
Fig. 10: The imitation learning experiment. (a) The robot must first locate the correct position of the box and then apply adequate force to press it. Excessive
force prevents movement, while insufficient force causes the fingers to slip. (b) The robot must first locate the bear, then open its hand to grasp it. Due to the
bear’s size, precise grasping control is required. An inaccurate grasp deforms the bear and causes it to slip out of the fingertips.
• Visual Obstacle: Grasping the carton is hindered by visual to the teddy bear slipping out of the robotic hand when not
obstacles, as the operator cannot see the contact points grasped firmly.
between the robotic hand’s fingers and the carton. Rotating and Placing the Carton. This task follows the same
Performance: Table III shows that both haptic and force setup as Section VI-C. For this contact-rich task, we use 3
feedback significantly improve the teleoperation success rate human-collected demonstrations to train the policy. Across 10
and reduce completion time. While force feedback alone trials, the success rate is 90% (9/10).
results in a comparable average completion time, haptic force
VII. L IMITATIONS AND F UTURE W ORK
feedback achieves a higher success rate. The vision-based Mo-
Cap method AnyTeleop [25] struggles with in-hand rotation DOGlove is a powerful haptic force feedback glove for
in this task. dexterous manipulation, but several limitations remain. First,
the weight of DOGlove is inevitably high, as it utilizes 5 com-
D. Imitation Learning mercial servos, bringing the total weight to 550g. Additionally,
in agile teleoperation scenarios, performance is constrained
We show DOGlove is capable of collecting high-quality by the servos’ maximum speed and torque output. To address
demonstrations. 3D Diffusion Policy (DP3) [45] is selected these issues, we are investigating the use of lighter servos with
as our imitation learning algorithm, and we use Realsense smaller reduction ratios and designing a customized reduction
L515 to acquire the point cloud inputs, which are then down- mechanism to balance speed and torque more effectively.
sampled to 1024 points using farthest point sampling [24]. The Second, although DOGlove is designed to accommodate most
data collected by DOGlove is used to train policies for various hand sizes, it may be uncomfortable for some users. To
downstream tasks. We evaluate imitation learning performance enhance adaptability and wearability, we are developing CAD
on 2 basic contact-rich tasks and 1 long-horizon task: files for linkages in multiple sizes, enabling customization for
Press and Move Box: As shown in Fig 10a, the robot must various hand dimensions.
continuously press down on a box and move it to a specified
target location. During data collection, the box is randomly VIII. C ONCLUSION
placed within a 30×20 cm area, and DOGlove collects 40 In this paper, we present DOGlove, a low-cost, open-
demonstrations to train the policy. In evaluation, the box is source haptic force feedback glove designed for dexterous
also randomly placed in the same area. Across 20 trials, the manipulation. DOGlove enables precise and efficient execution
success rate is 85% (17/20). of long-horizon, contact-rich tasks. Experimental results show
Pick and Place Teddy Bear: As shown in Fig 10b, the that DOGlove enhances the operator’s immersive teleoperation
robot must grasp a teddy bear and place it into a designated experience while also serving as an effective tool for training
box. During data collection, the teddy bear’s initial position is imitation learning policies. Moreover, the user study demon-
randomized within a 30×20 cm area, and DOGlove collects strates that DOGlove provides precise perception of object
40 demonstrations to train the policy. In evaluation, the bear properties through its integrated haptic force feedback. To
is again randomly placed in the same area. Across 20 trials, support further research and contribute to the community, all
the success rate is 70% (14/20), with failures primarily due hardware designs and code will be open-sourced.
ACKNOWLEDGMENT Proceedings of Robotics: Science and Systems (RSS),
We would like to thank Zhengrong Xue, Gu Zhang, Changyi 2024.
Lin, Mengda Xu, and Yifan Hou for their invaluable advice [9] Dexta Robotics. Dexta robotics official website. https:
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for their insightful discussions and feedback. Additionally, Shiqi Yang, Ruihan Yang, Xiaojuan Qi, and Xiaolong
we thank Yichuan Gao, Xiaoyan Yang, Xinyao Qin, and Wang. Bunny-visionpro: Real-time bimanual dexterous
Botian Xu for their assistance with the user study. Special teleoperation for imitation learning. arXiv preprint
thanks to Skyentific, Gennady Plyushchev, for their innovative arXiv:2407.03162, 2024.
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