Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation
Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation
This research evaluates how pretrained tactile foundation models can be leveraged for end-to-end imitation learning in contact-rich manipulation tasks. The authors integrate multiple tactile foundation models into Diffusion Policy and systematically assess their effectiveness across diverse manipulation tasks in both real-world and simulation environments, including more than 5,000 real-world policy rollouts. They also investigate adaptation strategies such as encoder fine-tuning and task-specific mid-training to understand how pretrained representations can be effectively used for downstream imitation learning.
The study integrates multiple tactile foundation models into Diffusion Policy to evaluate their effectiveness for end-to-end imitation learning in contact-rich manipulation, supported by over 5,000 real-world policy rollouts and simulation experiments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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e representations can be effectively used for downstream imitation learning. Through extensive evaluation across diverse manipulation tasks in both real-world and simulation environments, including more than 5,000 real-world policy rollouts, we provide empirical insights into the use of TFMs for end-to-end imitation learning. Project page: https://mmurooka.github.io/tfm4il-project-page/
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Source：arXiv Robotics — research abstracts · arxiv.org