SenseGlove R1 — Haptic Gloves for Tele-Robotics

Name: SenseGlove R1 — Haptic Gloves for Tele-Robotics
Brand: SenseGlove
Price: 17300.0 USD
Availability: InStock

by SenseGlove

$ 17,300.00 - $ 70,000.00

$ 17,300.00

$ 17,300.00 - $ 70,000.00

$ 17,300.00

Quantity

Overview

Control a robotic hand as if it were your own — and feel exactly what it feels. The SenseGlove R1 is a professional haptic interface engineered for tele-operation and imitation learning. It delivers millimeter-level finger tracking and real-time bidirectional force feedback at robotics-grade precision.

This is not a VR peripheral. The R1 creates a live feedback loop between a human operator and a physical robotic hand — the robot's applied forces, grip pressures, and contact events are transmitted back to your fingers in real time through a 1 kHz control loop.

Specifications

Finger tracking: 20 DoF, millimeter-level precision
Update rate: ~1 kHz
Latency: 10 ms (R1 to PC)
Active force feedback: 4 actuators per glove
Vibrotactile feedback: 8 actuators per glove
Force sensing range: 10 – 2,500g per finger
Interface: External tracker or grounded haptic arm
Software: Python API, ROS 2, SenseCom
Configuration: Left + right set standard — single hand option available

Features

Active force feedback with real-time pressure sensing A 1 kHz control loop renders the robot's applied forces to the operator's fingers instantly. When picking up a fragile object, the glove replicates that delicate pressure — ensuring safe, precise manipulation at all times.

Millimeter-level finger tracking 20 DoF tracking across all five fingers with mm-level accuracy. Perform micro-assembly, precision maintenance, and complex dexterous tasks with full confidence and repeatability.

Vibrotactile feedback Eight vibrotactile actuators deliver immediate cues about contact location, texture, and intensity — letting operators feel the difference between surfaces and receive real-time contact alerts.

Flexible deployment Use standalone with an external tracker or mount to a grounded haptic arm. Adaptable to lab, industrial, or research environments without additional infrastructure.

Python API and ROS 2 support Native integration with industry-standard robotics software stacks. Drops into existing ROS 2 pipelines with minimal overhead. Python API enables rapid prototyping and custom workflows.

Customization available SenseGlove works with enterprise customers on custom R1 configurations — incorporating specific sensors, form factors, or capabilities tailored to your robotic system.

Use Cases

Imitation learning Collect high-quality training data for robotic manipulation AI. Force feedback produces more natural human demonstrations and richer training datasets — research has shown haptic feedback improves data collection speed by 6% and AI model accuracy by 11%.

Direct tele-operation One-on-one control of dexterous robotic hands for precision tasks — micro-assembly, lab work, hazardous environment operations, or surgical assistance research.

Remote robotic fleet management Operate and error-correct robotic fleets from a remote location. A single skilled operator can supervise and correct multiple robots with full tactile awareness.

Robotics research and R&D Academic and research programs studying dexterous manipulation, human-robot interaction, haptic perception, and AI-driven robot learning.

Available Packages

Academic / R&D — $17,300 per set For universities, research institutes, and R&D labs.

One set of R1 gloves (left + right)
Academic license: SenseCom, ROS 2, Python API
Optional single-hand configuration available ($10,650)
Self-integration by your team

Industry Exploration — from $38,000 per project For companies evaluating tele-robotics feasibility.

One set of R1 gloves
3-week preparation project with SenseGlove team
1-week onsite hackathon — integrated into your system
2-year license for all SenseGlove software and APIs
Additional sets at standard pricing after the project

Industry Implementation — from $70,000 per project For organizations ready to deploy a full tele-op or imitation learning system.

End-to-end design and implementation
Integration with a single robotic hand (Seed, Tesollo, or equivalent)
Custom scoping available — contact us for requirements

Note: Enterprise projects include a mandatory integration fee to ensure correct configuration and validated performance. Lead time is 4–6 months from confirmed order. Production runs in committed batches — contact us to secure your position.

SenseGlove R1 vs. Nova 2

	R1	Nova 2
Primary use	Robotics tele-operation, imitation learning	VR haptic training and simulation
Works with	Physical robotic hands	VR headsets and virtual environments
Force feedback	Bidirectional — robot forces transmitted to operator	Active resistance against virtual objects
Vibrotactile	8 actuators	6 actuators
Software	Python API, ROS 2	Unity, Unreal, SenseGlove SDK
Entry price	$17,300 / set	$7,190 / set
Sale model	Quote — project engagement	Direct purchase
Integration	High — robotics expertise required	Low — standard XR SDK
Lead time	4–6 months	Standard availability

If your use case is VR-based training, simulation, or enterprise soft skills — the Nova 2 is the right product. If you are working with physical robots, collecting imitation learning data, or building tele-operation systems — the R1 is what you need. Not sure? Contact us and we will confirm the right device for your program.

Available through Knoxlabs — US-based XR solutions partner. Request a quote or schedule a consultation at hello@knoxlabs.com

SenseGlove R1 — A++ Content

Available now — selected partners

The haptic interface that closes the gap between human hands and robotic ones.

The SenseGlove R1 creates a live bidirectional feedback loop between operator and robot — transmitting force, pressure, and contact data in real time. Built for tele-operation, imitation learning, and robotic research programs that need measurable results.

20 DoF

Finger tracking

~1 kHz

Update rate

10 ms

R1-to-PC latency

2500 g

Max force per finger

Engineering overview

Every component engineered
for robotics-grade precision.

The R1 is not adapted from a VR training glove. It was designed from the ground up for telerobotics — with an internal force-sensing control loop, sub-millimeter motion capture, and a 1 kHz feedback rate that matches the demands of real robotic hand control.

SenseGlove R1 engineering diagram — annotated components

~1 kHz sample rate Sub-mm precision finger tracking Integrated force-sensing capabilities 5 DoF active force feedback with force-control Integrated vibrotactile feedback Standalone or mountable to haptic arm

Market context

The telerobotics market is accelerating.
Intuitive human control is the bottleneck.

Telerobotics by 2032

$286B

Up from $71.2B in 2023 — 18.4% CAGR driven by industrial automation and defense.

Humanoid robotics CAGR

50%

From $1.8B to $13.7B by 2028. Imitation learning is the primary robot training method.

Service robots by 2029

$98B

Nearly double its 2024 value. Service and collaborative robots require precise haptic control.

SenseGlove clients

500+

Across 50 countries since 2017. The R1 is built on nearly a decade of validated haptic research.

As robots grow more capable, intuitive human control becomes the bottleneck. Imitation learning is the dominant training paradigm — but it only works when the demonstrator can feel what the robot feels.

Traditional tele-op relies on visual feedback alone. Force data is entirely absent from most systems — a precision gap that neither VR controllers nor standard motion trackers can close.

The problem with current robot training

Imitation learning captures what you move — not what you feel. That missing data is costing you model performance.

Current dexterous robot training relies entirely on motion and vision. Force perception is completely invisible to the system. Without gripping force data, robots cannot understand how much pressure they apply when grasping — leading to failed manipulations, damaged objects, and AI models that struggle to generalize. The R1 closes this gap by feeding real force data into the training loop in real time.

Failed grasps from uncalibrated force

Robots apply inconsistent pressure — crushing fragile items or failing to hold objects securely.

Incomplete AI training data

Models trained without force data cannot generalize — the demonstrations are missing half the signal.

Safety gaps in hazardous environments

Without haptic feedback, operators cannot judge material resistance — increasing risk in nuclear, chemical, and defense applications.

Core capabilities

Three systems. One precise feedback loop.

Capability 01

Active force feedback with real-time pressure sensing

A 1 kHz control loop renders the robot's applied forces to your fingers instantly. When picking up a fragile object, the glove replicates that exact pressure — ensuring safe, precise manipulation. Research confirms this bidirectional feedback produces measurably better AI training data.

4×active force actuators per glove

Capability 02

Millimeter-level accuracy across all five fingers

20 degrees of freedom tracked at sub-millimeter precision. Every finger movement — including fine lateral motions — is captured faithfully and mapped directly to the robotic hand. Perform micro-assembly, precision maintenance, and complex dexterous tasks with full repeatability.

20DoF tracked per hand

Capability 03

Vibrotactile feedback for contact and texture sensing

Eight vibrotactile actuators deliver immediate cues about contact location, surface texture, and interaction intensity. Feel the difference between materials, receive real-time contact alerts, and gain tactile situational awareness that pure visual feedback cannot provide.

8×vibrotactile actuators per glove

Evidence-based results

Peer-reviewed research.
Measurable outcomes.

Researchers have studied whether haptic feedback in teleoperation actually produces better robots. The evidence is consistent across independent labs and different robot platforms: force feedback improves data quality, model accuracy, and operator precision. These three studies quantify it precisely.

IEEE — Study 01

"Leveraging haptic feedback to improve data quality and quantity for deep imitation learning models"

faster training data collection by operators with haptic feedback

11%

improvement in AI model accuracy on autonomous door-opening tasks

When operators felt what the robot was doing, they naturally adjusted their demonstrations — producing richer datasets and smarter autonomous models.

ResearchGate — Study 02

"Haptic-ACT: bridging human intuition with compliant robotic manipulation via immersive VR" — using SenseGlove

15%

lower contact forces in robots trained with haptic-enhanced imitation learning

30%

less thumb force applied — more natural, compliant grasps

Haptic force feedback-enhanced learning improved robot accuracy and made grasps more compliant — reducing the risk of damaging fragile or deformable objects.

ResearchGate — Study 03

"Leveraging VR and force-haptic feedback for an effective simulation training with robots" — Kuka robot, SenseGlove

85%

of participants rated haptic force feedback as highly realistic

participants with zero prior robotics experience — all successfully trained manipulation

Prior experience had no significant impact on ability to learn — making haptic tele-op accessible to operators without specialist backgrounds.

In practice

Organizations already deploying
SenseGlove in the field.

From nuclear cleanup to defense operations — real programs solving high-stakes tele-robotic challenges.

ORNL

Oak Ridge National Laboratory

U.S. Department of Energy

Nuclear

Challenge

Hazardous and toxic waste handling — workers at risk in high-exposure environments

Integrated SenseGlove into their teleoperated robotic system. Operators now feel the exact pressure needed to manipulate hazardous materials remotely — improving cleanup accuracy and eliminating direct worker exposure.

EXR

Extend Robotics + QuarkXR

Industrial Automation

Accessibility

Challenge

Tele-operation required specialist expertise — limiting who could effectively control remote systems

Integrating SenseGlove into their XR platform eliminated the need for extensive training. A far broader range of users can now control robots intuitively from any location.

TNO

TNO Netherlands

Applied Scientific Research

Defense

Challenge

Military and emergency response — lack of haptic feedback led to inaccurate control and poor situational awareness

Built a Haptic Bimanual Telexistence System using SenseGlove DK1. Operators gained realistic resistance feedback — improving precision and safety in high-stakes critical operations.

Trusted by leading research, government, and defense organizations

NASA Marshall Space Flight Center Oak Ridge National Laboratory U.S. Department of Energy Dutch Ministry of Defence TNO Netherlands Emirates University of Bonn Extend Robotics

University of Bonn used SenseGlove to win the $10M ANA Avatar XPrize — the world's premier telerobotics competition

Research Whitepaper

Haptic Feedback in Telerobotics: Bridging the Gap Between Human and Machine

Free download

The research guide to haptic force feedback in telerobotics.

22 pages covering the evidence, deployments, and market data. Essential reading before evaluating any force feedback system for robotics programs.

3 peer-reviewed studies with quantified outcomes

Case studies: NASA, ORNL, TNO, Extend Robotics

Telerobotics and humanoid robotics market data

Technical analysis of imitation learning and VR tele-op

Limited 2026 production batch — 4–6 month lead time from confirmed order

Ready to secure your R1 position for 2026?

The R1 is produced in committed batches. If your program needs delivery this year, now is the time to confirm. We start with a 30-minute consultation — no generic quotes, no pressure.

hello@knoxlabs.com

Response within 1 business day

Request a quote

Or email hello@knoxlabs.com