Zhiyun Lin

Junlin Wang, Zhiyun Lin

Learning effective visual representations for robotic manipulation remains a fundamental challenge due to the complex body dynamics involved in action execution. In this paper, we study how visual representations that carry body-relevant cues can enable efficient policy learning for downstream robotic manipulation tasks. We present $\textbf{I}$nter-token $\textbf{Con}$trast ($\textbf{ICon}$), a contrastive learning method applied to the token-level representations of Vision Transformers (ViTs). ICon enforces a separation in the feature space between agent-specific and environment-specific tokens, resulting in agent-centric visual representations that embed body-specific inductive biases. This framework can be seamlessly integrated into end-to-end policy learning by incorporating the contrastive loss as an auxiliary objective. Our experiments show that ICon not only improves policy performance across various manipulation tasks but also facilitates policy transfer across different robots. The project website: https://github.com/HenryWJL/icon

Liang Xu, Tao Liu, Zhiyun Lin

This paper investigates the robust output regulation problem for an uncertain linear minimum-phase plant with cooperative parallel operation of multiple actuators. Building on the internal model approach, we first propose a dynamic output feedback control law to solve the robust output regulation problem with a single actuator. Then, we construct a distributed dynamic output feedback control law that is nearly independent of the number of actuators and incorporates coupling terms to address the linear robust output regulation problem with cooperative parallel operation of multiple actuators over undirected communication networks. We reveal the connection in the design of parameters between the dynamic output feedback control law under single actuator operation and the distributed dynamic output feedback control law under cooperative parallel operation with multiple actuators. Moreover, we remove the existing assumption that the actuator dynamics must be Hurwitz stable, thereby enabling the incorporation of unstable actuators in our framework. Finally, two numerical examples are provided to validate the effectiveness of the proposed control laws.