Noitom Robotics Introduces ITW-Ego Plus at CoRL 2026: In-the-Wild Egocentric Data With Full-Body Motion

Three main-track papers — HiPHI, OmniContact and AdaPT — presented Nov. 10; NEAR Lab research unit introduced; Booth 1105, with private capture demonstrations by appointment

• ITW-Ego Plus introduced at CoRL 2026 (Austin, Nov. 9–12, Booth 1105): a new category of in-the-wild human data that pairs egocentric video with full-body motion and other modalities, captured with Sol and Jupiter, both developed in-house by Noitom Robotics.
• Three main-track papers — HiPHI, OmniContact and AdaPT — presented Nov. 10 by NEAR Lab, the Noitom Robotics Embodied-AI Research Lab introduced at CoRL under Dr. Han Lei, with the lab’s Unified Motion Retargeting (UMR) research validated on five humanoids.
• HiPHI-Hand released openly on Hugging Face at CoRL: 11 hours of egocentric video with marker-based 3D hand ground truth, produced for NVIDIA’s hand-reconstruction research and processed by NVIDIA as a real-world source for training NVIDIA EgoHand, demonstrated live at Booth 1105. HiPHI itself has passed 15,000 downloads.
  

AUSTIN, Texas, Nov. 05, 2026 (GLOBE NEWSWIRE) -- Noitom Robotics (NR), a Physical AI data infrastructure company, will present three accepted papers at the Conference on Robot Learning (CoRL 2026) in Austin, Nov. 9–12, and introduce ITW-Ego Plus, a new category of In-the-Wild (ITW) human data that pairs egocentric video with full-body motion and other modalities captured in real environments. NR exhibits at Booth 1105.

A Media Snippet accompanying this announcement is available by clicking on this link.

Egocentric video is now widely available. What remains scarce is in-the-wild data that is learnable: first-person video paired with body motion, viewpoints and contact signals. NR's ITW line has two products: ITW-EgoVision, egocentric vision captured on NR's Sol headset, and ITW-Ego Plus, which adds modalities à la carte: Body (full-body motion), Wrist (wrist-perspective video), Exo (fixed third-person views of task and user), Touch (hand pressure) and Sound (environmental audio).

At CoRL, NR shows Body. Powered by Sol and Jupiter, both developed in-house by NR, the ITW-Ego Plus capture system connects first-person visual context with whole-body movement. Jupiter — a few small, discreet wearables — captures full-body motion without a motion-capture suit, so people perform tasks in real environments as they normally would. The hardware, and sample data captured with it, are on display at Booth 1105.

"Egocentric video is the easy half of in-the-wild data. The hard half is the body behind the camera — where the person stood, how they reached, how they moved through the task," said Dr. Tristan Ruoli Dai, Founder and CEO of Noitom Robotics. "Outside a lab, that has meant a suit or a room full of trackers. ITW-Ego Plus records it with a handful of small wearables. Scale gives you the first half; the second half is what makes scale learnable."

Three papers in the CoRL 2026 main track

NR's research team and its academic collaborators will present three accepted papers, all built on HiPHI (High Precision Human Interaction) data, at the poster session on Tuesday, Nov. 10, at 5:00 p.m. at the JW Marriott Austin:

• HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction (Jiahao Ji et al.) — Uses FrameNet to plan what to capture: 617.5 hours of whole-body motion and real-object interaction, evaluated from motion coverage through to deployment on real humanoids.
• OmniContact: Chaining Meta-Skills via Contact Flow (Runyi Yu et al.) — Contact flow links task-level reasoning to whole-body execution, so humanoids chain skills into long-horizon tasks. Trained on the HiPHI-MOV (Motion with Object and Vision) corpus.
• AdaPT: Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking (Tao Huang, Ruofei Liu et al.) — Humanoid tennis in professional styles, learned from broadcast video. Pre-trained on part of the HiPHI-series data, including a relevant subset of the publicly released HiPHI dataset.
  
  

NEAR Lab and UMR

The papers come from NEAR Lab, the Noitom Robotics Embodied-AI Research Lab, which NR introduces at CoRL. NEAR Lab is led by Dr. Han Lei, Chief Scientist. Its most recent work, Unified Motion Retargeting (UMR, arXiv 2609.02134), learns dense surface correspondence between human and robot bodies and retargets any mesh-based motion to any humanoid; it was validated on five robots from 0.75 m to 1.83 m. UMR is a step toward delivering ModalityNet motion data as robot-ready references for any humanoid.

"NEAR Lab exists to close the loop between what we capture and what a robot can learn from it," said Dr. Han Lei. "HiPHI plans the capture, OmniContact and AdaPT train on it, and UMR carries the motion to the next robot. CoRL is where we put all four in front of the people who will use them."

Open data

HiPHI, the open research subset of the HiPHI-MOV corpus released in August under the ModalityNet Open Research License (MORL) v1.0, free for research, has passed 15,000 downloads across Hugging Face and NR’s distribution partners. The full HiPHI-MOV corpus is licensed commercially through ModalityNet.

HiPHI-Hand, which NR releases on Hugging Face at CoRL under the same license, adds hand-level precision to the same family of openly released data. NR produced the capture for NVIDIA's hand-reconstruction research: 11 hours of synchronized egocentric video from the Sol headset with marker-based 3D hand ground truth at sub-millimeter precision, captured with an optical motion-capture system using micro-markers on the fingers. NVIDIA processed this capture into its own training representation and used it as one of the real-world sources for training NVIDIA EgoHand, its 3D hand reconstruction model for egocentric video. HiPHI-Hand is the capture as NR delivered it.

At the booth

At Booth 1105, NR shows samples from the HiPHI-MOV and HiPHI-OM (Omni-Manipulation) corpora, ITW-Ego Plus data captured with Sol and Jupiter, and the Sol and Jupiter hardware. Attendees can also wear the Sol headset and see their own hands reconstructed in 3D in real time by EgoHand running on NVIDIA hardware, in a demonstration built by the NR research team. Live capture demonstrations of Sol and Jupiter take place in a private suite, by prior appointment. Research and robotics teams can request an appointment at contact@noitomrobotics.com. Dr. Dai will attend CoRL and is available for meetings by appointment.

About Noitom Robotics
Noitom Robotics is a data company for Physical AI. We build the World Compiler — the infrastructure layer that transforms real-world human interaction into machine-learnable representations for embodied intelligence. Our implementation is ModalityNet (modalitynet.com), a large-scale, human-centric, omnimodal data substrate built on three complementary corpora: HiPHI-MOV (High Precision Human Interaction — Motion with Object and Vision), capturing high-fidelity whole-body motion and loco-manipulation; HiPHI-OM (High Precision Human Interaction — Omni-Manipulation), capturing omni-modal interaction including contact, force, tactile, and object-state signals; and ITW-EgoVision & Ego Plus (In-The-Wild), capturing natural human behavior at scale in unconstrained real-world environments. Together they address the core bottleneck of Physical AI — not data scarcity, but learnability — by using compact, fully structured corpora to make vastly larger, weakly structured data learnable. Our mission is simple: make the physical world learnable.

Media contact
Roch Nakajima, CMO, Noitom Robotics — roch@noitomrobotics.com
Evan Tan, Director of Marketing, Noitom Robotics — evan@noitomrobotics.com

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/388f3774-730e-49a9-a36a-cc90353fab9d

ITW-EgoPlus

ITW-Ego Plus by Noitom Robotics pairs in-the-wild egocentric video with full-body motion and other modalities — the body behind the camera, captured where people actually work — so in-the-wild data becomes learnable for embodied AI.