Press Releases June 30, 2026 09:00 AM

Wetour Robotics (NASDAQ: WETO) Demonstrates Conductor Neural Wristband with Training Powered by Meta’s Open emg2pose Dataset to Advance Physical AI Human-Machine Interaction and future physical-world models

Wetour Robotics showcases Conductor neural wristband enabling real-time 3D hand pose digital twins and gesture-to-text commands using Meta's open dataset

By Jordan Park
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Wetour Robotics Ltd. (NASDAQ: WETO) demonstrated its Conductor neural wristband, which decodes wrist muscle signals into real-time 3D hand poses and converts gestures into text commands, leveraging Meta's open emg2pose dataset. The Conductor, part of Wetour's Orchestra platform, is designed for affordable, on-device deployment with 8-channel sensors and enables privacy-preserving, real-time human-machine interaction data collection to advance Physical AI systems and robotics applications. The company has opened an Early Access Program for enterprise partners to develop connected machine interfaces using this technology.

Wetour Robotics (NASDAQ: WETO) Demonstrates Conductor Neural Wristband with Training Powered by Meta’s Open emg2pose Dataset to Advance Physical AI Human-Machine Interaction and future physical-world models
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Key Points

  • Conductor wristband converts muscle signals into detailed 3D hand pose digital twins and real-time gesture-to-text commands without cameras or gloves.
  • Technology builds on Meta's open emg2pose dataset but adapts it for consumer-grade hardware with on-device edge computing, focusing on affordability and privacy.
  • Wetour Robotics positions Conductor as both a control interface and a data-collection tool for advancing Physical AI, wearable robotics, and human-machine interaction platforms.
  • Sectors impacted include robotics, wearable technology, AI-driven human-machine interfaces, consumer electronics, and healthcare technology for gesture-based control and data collection.

Live demo turns wrist muscle signals into real-time 3D hand digital twins and gesture-to-text commands — creating an on-device human-intent data layer for robotics

AUSTIN, Texas, June 30, 2026 (GLOBE NEWSWIRE) -- Wetour Robotics Ltd. (NASDAQ: WETO) (“Wetour” or the “Company”), a Physical AI and wearable-robotics infrastructure company, today released a new demonstration of Conductor, the sEMG (surface electromyography) neural wristband at the core of its Orchestra platform. In the demonstration, Conductor decodes muscle signals from an 8-channel wrist sensor into a real-time 3D hand pose — a live digital twin of the wearer’s hand, rendered with no cameras and no gloves.

Gestures into text, in real time. The demonstration also shows Conductor recognizing discrete hand gestures and converting them into text commands on screen in real time — turning deliberate gestures into typed input with no keyboard or touchscreen.

Demonstration videos are available at www.wetourrobotics.com and on the Company’s LinkedIn and X channels under Wetour Robotics and @WETO_IR_TEAM.

Built on open research, trained on WETO's own architecture. Decoding continuous sEMG signals into hand pose builds on a fast-moving research frontier pioneered by Meta and others, and WETO trains directly on Meta's openly released emg2pose dataset. The Company is deliberately clear that the underlying capability is shared, open research rather than a proprietary first — its work is about what comes next: making that capability practical, affordable, and private enough to wear every day.

Engineered for affordable, on-device deployment. Meta's research setup captures 16 channels at 2 kHz; Conductor targets a consumer-grade 8-channel band at 250 Hz. WETO bridges this gap in two stages: first, the model is pre-trained on the emg2pose dataset downsampled to 8 channels at 250 Hz to match Conductor's hardware, learning the core sEMG-to-pose mapping from a large, high-quality corpus; then it is adapted through transfer learning on WETO's own data, collected directly from the 8-channel, 250 Hz consumer band, so the model is fine-tuned to the exact sensor it will run on in the field. The architecture is a streaming, state-space (Mamba) model chosen for linear-time, constant-memory inference — designed to run fully on-device at the edge. In the demonstration, the model is evaluated on gestures it had not previously seen.

Open, cross-device by design. Conductor is built as part of Orchestra — WETO’s open, cross-device platform designed to turn human gesture into action across connected machines rather than running on a single vendor’s hardware. The Company’s positioning is direct: Your Body is the Interface.

A human-intent data layer for Physical AI. WETO sees the real-time hand-pose digital twin as more than an input demo. By translating muscle signals into hand pose, gesture commands, and real-time interaction events at the edge, Conductor is designed to help developers collect richer human-machine interaction data for robotics, connected devices, and future physical-world models — while keeping raw signals private and on-device.

From wrist input to robotics data collection. For enterprise partners, Conductor is positioned not only as a control interface, but also as a potential robotics data-collection endpoint for real-world human intent, gesture, and interaction data. WETO believes this type of wearable, on-device data layer can support a new generation of Physical AI systems that better understand how humans move, signal intent, and coordinate with machines in the physical world.

WETO’s enterprise Early Access Program is open to qualified partners, who receive Orchestra hardware samples, SDK access, and hands-on co-development support.

About Wetour Robotics Ltd. (NASDAQ: WETO)

Wetour Robotics Limited (NASDAQ: WETO) is a Physical AI infrastructure and wearable robotics company developing Orchestra — a portable AI hub and operating system. Orchestra’s sensory modules include VisionLink (computer vision), Conductor (sEMG-based neural gesture recognition), and Spatial Intent Fusion (pointing direction coordinated with neural gesture input). Headquartered in Austin, Texas. Visit www.wetourrobotics.com.

Forward-Looking Statements

This press release contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements include, but are not limited to, statements regarding the Company’s products, platform and demonstration capabilities, real-time hand-tracking, gesture-recognition and command-translation features, on-device and edge-deployment objectives, robotics data-collection use cases, human-machine interaction data, future physical-world models, the Early Access Program, target markets, and future plans. Demonstration results reflect controlled conditions, including evaluation on held-out gestures, and may not be representative of all use cases or production performance. References to third parties, including Meta Platforms, Inc., and to the openly released emg2pose dataset, are descriptive of the broader sEMG/neural-interface research field and the data used in development, and do not imply any affiliation, endorsement, partnership, sponsorship, authorization, or comparison of performance. Words such as “will,” “intend,” “expect,” “designed to,” “targets,” “believes,” and similar expressions identify forward-looking statements. Such statements are based on current expectations and are subject to risks and uncertainties that could cause actual results to differ materially, including those described in the Company’s filings with the U.S. Securities and Exchange Commission. The Company undertakes no obligation to update any forward-looking statement except as required by law.

Contact

Annabelle Li
Investor Relations
[email protected]


Risks

  • Demonstration results are from controlled conditions and may not represent all real-world use cases or production performance, posing execution risk.
  • Dependence on transfer learning from open datasets may limit competitive differentiation versus proprietary solutions in the rapidly evolving sEMG research field.
  • Market adoption depends on enterprise partners' willingness to integrate new wearable AI technologies and navigate privacy and usability challenges in human-machine interfacing.

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