Robotic System
Robotic systems research focuses on developing robots capable of performing complex tasks autonomously and reliably in diverse environments. Current efforts concentrate on improving robot perception (e.g., vision-language models for failure detection and reasoning), control (e.g., adaptive control for joint failures and force-aware trajectory planning), and planning (e.g., behavior tree expansion with LLMs and probabilistic automata for task specification). These advancements are significant for various applications, including manufacturing, agriculture, healthcare, and domestic assistance, driving improvements in efficiency, safety, and human-robot collaboration.
Papers
Risk-Aware Robotics: Tail Risk Measures in Planning, Control, and Verification
Prithvi Akella, Anushri Dixit, Mohamadreza Ahmadi, Lars Lindemann, Margaret P. Chapman, George J. Pappas, Aaron D. Ames, Joel W. Burdick
PhysicsAssistant: An LLM-Powered Interactive Learning Robot for Physics Lab Investigations
Ehsan Latif, Ramviyas Parasuraman, Xiaoming Zhai
Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based Control Barrier Functions
Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy, Farshad Khorrami
Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks
Hongchao Zhang, Luyao Niu, Andrew Clark, Radha Poovendran
Equivalent Environments and Covering Spaces for Robots
Vadim K. Weinstein, Steven M. LaValle
Generation of skill-specific maps from graph world models for robotic systems
Koen de Vos, Gijs van den Brandt, Jordy Senden, Pieter Pauwels, Rene van de Molengraft, Elena Torta