LingBot-Touch learns shared tactile tokens across sensor types
Original title: Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
Authors say the tokens plug into diffusion policies and VLA models, including pi0.5 and LingBot-VLA.
Researchers introduce LingBot-Touch, a tactile foundation model meant to represent piezoresistive, magnetic, and visuotactile sensor data in one token space and extend to new sensor types. The authors say the tokens can be added to policies ranging from diffusion policies to vision-language-action models such as pi0.5 and LingBot-VLA. Experiments span simulation and real robots on multiple hardware platforms, with reported gains in manipulation dexterity and strong computational efficiency.
Unconfirmed: Performance gains are reported by the paper's authors, not independently verified.
- The model covers piezoresistive, magnetic, and visuotactile sensors.
- Authors say the representation can extend to new sensor types.
- Tokens were tested inside diffusion policies and VLA models.
- Deployments include simulation and real robots on multiple platforms.
- AI models
- LingBot-TouchLingBot-VLApi0.5
From the original article
1 min read at huggingface.co
Tactile sensing is an indispensable modality for contact-rich dexterous manipulation, a capability that hints at the ultimate frontiers of embodied intelligence. However, commercially available tactile sensors are still at a relatively early stage of development, with each type of sensor offering irreplaceable strengths alongside its own drawbacks.
By Bing Han, Yichuan Peng, Haoyu Dong · huggingface.co
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