Imitation Learning (IL) from human teleoperation has emerged as a powerful approach for training robotic systems in complex manipulation tasks. However, challenges remain in developing effective strategies for data collection, ensuring standardization across diverse teleoperation setups, and integrating tactile sensing for improved learning from demonstration (LfD). This workshop will bring together leading researchers and practitioners to explore these key challenges, addressing the question: What are the best strategies to collect and standardize teleoperation data, and what is the role of tactile sensing and  haptic feedback in LfD?

Featuring expert speakers, the workshop will present insights on best practices for designing teleoperation interfaces, curating high-quality datasets, and leveraging tactile feedback for enhanced imitation learning. A poster session, complemented by selected lightning talks, will provide an interactive platform for participants to share innovative research, fostering collaboration and discussion on the future of LfD in robotics.