CLARU
hgupt32025MIT

Sensor-Invariant Tactile Representation (SITR) Dataset

A large-scale tactile perception dataset comprising 1M simulated samples across 100 sensor configurations and real-world classification/pose estimation data from 7 tactile sensors. The dataset enables training sensor-invariant tactile representations for zero-shot inference and fine-tuning on various tactile perception tasks.

Downloads122
Episodes1M (simulated) + 140K (classification) + 24K (pose estimation)
Likes1

Why This Matters for Physical AI

This dataset enables development of sensor-invariant tactile representations that can generalize across different tactile sensor hardware, a critical capability for deploying tactile perception in diverse robotic systems.

Technical Profile

Modalities
rgbtactiledepth
Environment
simulationlab
Task Types
tactile_classificationpose_estimationfeature_extraction
Episodes
1M (simulated) + 140K (classification) + 24K (pose estimation)
Data Format
PNG images, NPY arrays (depth maps, surface normals, pose data)
Annotation Types
class_labelspose_labelscalibration_images
License
MIT
Part of the SITR family

Access

View on HuggingFaceRead Paper

Need custom rgb data?

Claru builds purpose-built datasets for simulation applications with dense human annotations and quality assurance.

Request a Sample Pack

Related Datasets

Xperience-10M

A large-scale egocentric multimodal dataset of human experience containing 10 million interactions and 10,000 hours of synchronized first-person recordings with six video streams, audio, stereo depth, camera pose, hand mocap, full-body mocap, IMU, and hierarchical language annotations for embodied AI, robotics, and world modeling research.

rgbaudiodepthproprioception+3
2.3M downloadsother

Open-H-Embodiment

A multi-embodiment community-driven dataset of paired kinematics and video for training AI autonomy models in surgical robotics and ultrasound applications. The dataset includes tabletop exercises, clinical procedures, and simulations of healthcare robotics tasks.

rgbproprioception
118K downloads2026cc-by-4.0

Open-H-Embodiment

A community-driven, multi-embodiment dataset of paired kinematics and video for training and evaluating AI autonomy models in surgical robotics and ultrasound applications, including tabletop exercises, clinical procedures, and healthcare robotics simulations.

rgbproprioception
76K downloads2026cc-by-4.0

LingBot-Depth Dataset

Self-curated RGB-D dataset for training masked depth modeling approaches, containing real-world indoor scenes, VLA robot manipulation tasks, and simulated data across multiple camera types and robot platforms.

rgbdepth
71K downloadsCC BY-NC-SA 4.0

PhysicalAI-Robotics-Manipulation-Kitchen-Demos

A large-scale dataset of 600 hours of human-teleoperated demonstrations across 316 different kitchen manipulation tasks, totalling 55k trajectories collected using a Franka Panda robot with an Omron mobile base.

rgbproprioception
57K downloads2025cc-by-4.0

OmniAction

A large-scale multimodal dataset for proactive robot manipulation comprising 141,162 episodes with cross-modal contextual instructions derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands.

rgbaudiolanguage
45K downloads2025cc-by-nc-4.0