Title: Embracing Contact: Leveraging Physical Interactions for Enhanced Robotic Control

This thesis addresses key challenges in learning and control for physical interaction for agile robots and dexterous manipulation. Specifically, it investigates how to effectively capture human demonstrations of contact-rich and dynamic tasks and how to translate them into robot capabilities. Analysis reveals that relatively simple models can sufficiently represent human dynamic interactions at an abstract level (e.g., hand motion and contact forces relative to the center of mass). Combined with reflex-like controllers, these simple models can be used to recreate dynamic physical interaction behaviors in robots.

Building on these insights, this thesis extends these principles to dexterous manipulation with soft robotic hands. Just as the human body leverages compliance to safely and effectively interact with the environment, soft hands offer compliance and robustness through their inherent material flexibility. However, unlike the human hand, which relies on rich multimodal sensing, soft robot hands are usually limited by a lack of reliable sensing. To address this, we first develop a multimodal sensing and learning approach for tendon-driven soft fingers, combining actuation-side and embedded sensing to predict joint angles, contact forces, and contact locations. Through systematic ablations, we show that accurate state estimation can be achieved with minimal sensing, and that tendon-force measurements alone provide a strong signal for interaction understanding.

We then move toward a fully sensorless paradigm, introducing a learning-based framework that infers hand state and contact events directly from motor-side signals only. By combining a learned underactuation model with a multi-task temporal network, the approach predicts joint configurations, contact forces, and contact locations without any sensors on the hand, and generalizes across fingers via zero-shot transfer.

Finally, these models are integrated into a teleoperation system with haptic feedback, enabling users to perceive contact and grasp events without visual input. More broadly, this work demonstrates that accurate perception of contact-rich interactions can emerge from actuation signals alone, reducing the need for complex and fragile sensing hardware. This opens a path toward simpler, more robust, and scalable robotic systems capable of operating in unstructured, real-world environments, where reliable sensing remains a key bottleneck in order to embrace contact.