THE TACMIND BENCHMARK
TacMind: Benchmarking and Modeling Tactile Interaction States for Robotic Manipulation
“Grasp the felt eraser, erase the TacMind lettering from the rectangular whiteboard, then lift the eraser clear.”
Dynamic · Extra
Object assets
Unique object assets, including targets and cut-food pieces. Scene fixtures are excluded.
Robotic manipulation tasks
128 tasks across insertion, recognition, dynamic interaction, and more. Standard tasks include GSmini and Daimon sensor recordings.
Precision insertion
Insert tubes, keys, connectors, gears, power plugs, and bulbs into their target fixtures.
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EVALUATION CONDITIONS
Evaluation perturbations
Evaluate trained policies under changes in lighting, camera viewpoint, and object scale.
Warm lighting
2700 K lighting preset.
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Left: nominal baseline. Right: selected perturbation. Each preview changes one condition at a time, across three Standard tasks. Static scenes · seed 0.
TACMIND-BENCH · GELSIGHT MINI
Leaderboard
Performance across precision, recognition, and dynamic manipulation.
| Model | Precise | Recognition | Dynamic | Avg | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| InD | Light | Cam | Size | InD | Light | Cam | Size | InD | Light | Cam | Size | InD | OOD ↓ | |
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InD In-distributionOOD Out-of-distributionLight / Cam / Size Lighting, camera, and object-size perturbations
ACT+Tactile and DP use single-task training: a separate policy is trained for each task.
Results from the table. Bold values mark the best score in each column. Scroll horizontally to explore all metrics.
TODO
We will continue developing TacMind-Bench:
- Support a wider range of tactile sensors.
- Design more tasks centered on tactile interaction.
News
Updates from TacMind.
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Acknowledgements
We thank the contributors to the following open-source projects for sharing their tools, datasets, models, and research with the robotics community.