Low Level

Low-level robot control focuses on translating high-level instructions (e.g., "open the door") into precise, real-time actuator commands. Current research emphasizes bridging the gap between high-level planning (often using large language models or hierarchical reinforcement learning) and low-level execution, exploring methods like diffusion models, adaptive controllers, and multi-agent systems to improve robustness, efficiency, and generalization across diverse tasks. This work is crucial for advancing the capabilities of robots in complex, unstructured environments and has significant implications for various applications, including manufacturing, healthcare, and domestic assistance.