4 papers · 1 filter
TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems
Zexin Li, Soheil Shirvani, Cong Liu
Autonomous robotic systems, including autonomous vehicles, drones, and mobile robots, increasingly rely on on-device Deep Reinforcement Learning (DRL) to adapt to dynamic environme…
Orion: Enabling Self-adaptive Memory Management for On-device Online Continual Learning
Zexin Li, Nikil Dutt, Cong Liu
Online continual learning (OCL) enables real-time adaptation to new data, making it crucial for dynamic robotic applications. However, its practical deployment is hindered by memor…
RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
Zexin Li, Tao Ren, Johnathan Liu +2
Robots deployed in dynamic environments must contend with environment-driven changes that reshape computation at runtime: new tasks may appear, precedence relations can shift, and…
PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
Zexin Li, Ziliang Zhang, Hyoseung Kim +1
Recent research has demonstrated the potential of reinforcement learning in effective multi-robot collaboration, particularly in social dilemmas where robots face a trade-off betwe…