Cluttered Environment

Cluttered environment research focuses on enabling robots to navigate, manipulate objects, and perform tasks in complex, obstacle-filled spaces. Current research emphasizes developing robust perception systems (often using convolutional neural networks, transformers, and vision-language models) and efficient planning algorithms (including A*, reinforcement learning, and optimization-based methods) for various robotic platforms, from drones to mobile manipulators. This work is crucial for advancing robotics in diverse fields, including warehouse automation, search and rescue, and assistive technologies, by improving the adaptability and reliability of robots in real-world scenarios. The development of large-scale datasets and standardized benchmarks is also a significant focus, facilitating the comparison and improvement of different approaches.