Modal
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Modal, in robotics and AI contexts, refers broadly to the representation and processing of distinct types or modes of information—whether sensory modalities (vision, touch, sound, force), structural vibration modes in mechanical systems, or multiple discrete behavioral states. In structural and manipulator dynamics, modal analysis decomposes a system's vibration into characteristic mode shapes and frequencies, enabling engineers to model flexibility, suppress unwanted oscillations, and improve control of robotic arms and flexible structures. In perception and human-robot interaction, multi-modal approaches fuse data from heterogeneous sensors—cameras, tactile arrays, microphones, depth sensors—allowing robots to build richer environmental representations than any single sensing channel provides. In motion planning and prediction, multi-modal frameworks account for the inherent uncertainty of future states by modeling multiple plausible outcomes simultaneously. Modal concepts matter because real-world robotic systems must cope with mechanical compliance, ambiguous sensory data, and unpredictable environments; treating each of these challenges through a unified modal lens—decomposing complex signals into interpretable components—enables more robust perception, safer physical interaction, and more adaptable autonomous behavior across domains from surgical robotics to autonomous navigation.
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Top Cited Papers
A modal approach to hyper-redundant manipulator kinematics
Gregory S. Chirikjian, Joel W. Burdick
Citations: 584 • 1994
A comparative review on multi-modal sensors fusion based on deep learning
Qin Tang, Jing Liang, Fangqi Zhu
Citations: 236 • 2023
The other question: can and should robots have rights?
David J. Gunkel
Citations: 224 • 2017
An adaptive input shaping control scheme for vibration suppression in slewing flexible structures
Anthony Tzes, Stephen Yurkovich
Citations: 220 • 1993
Structural health monitoring of the Tamar suspension bridge
Ki Young Koo, James Brownjohn, David List, Richard Cole
Citations: 202 • 2012
Glowworm swarm optimisation: a new method for optimising multi-modal functions
K.N. Krishnanand, Debasish Ghose
Citations: 192 • 2009
Multi-modal locomotion: from animal to application
R J Lock, Stuart C Burgess, Ravi Vaidyanathan
Citations: 184 • 2013
BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation
Yu Yao, Ella Atkins, Matthew Johnson‐Roberson, Ram Vasudevan, Xiaoxiao Du
Citations: 184 • 2021
Modelling the dynamics of industrial robots for milling operations
Hoai Nam Huynh, Hamed Assadi, Édouard Rivière-Lorphèvre, Olivier Verlinden, Keivan Ahmadi
Citations: 178 • 2019
Generating the Future with Adversarial Transformers
Carl Vondrick, Antonio Torralba
Citations: 175 • 2017
Dynamic characterization of machining robot and stability analysis
Seifeddine Mejri, Vincent Gagnol, Thien-Phu Le, Laurent Sabourin, Pascal Ray, Patrick Paultre
Citations: 170 • 2015
Modal Control Of An Attentive Vision System
James J. Clark, Nicola Ferrier
Citations: 166 • 2005
IFAC 75: 6th triennial world congress
Citations: 155 • 1975
Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion
Koh Hosoda, Takashi Takuma, Atsushi Nakamoto, Shinji Hayashi
Citations: 152 • 2007
The Acquisition of Modal Concepts
Brian Leahy, Susan Carey
Citations: 150 • 2019
Multi-modal Semantic Place Classification
Andrzej Pronobis, Óscar Martínez Mozos, Barbara Caputo, Patric Jensfelt
Citations: 147 • 2009
Randomized multi-modal motion planning for a humanoid robot manipulation task
Kris Hauser, Victor Ng‐Thow‐Hing
Citations: 145 • 2010
Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach
Luka Peternel, Tadej Petrič, Erhan Öztop, Jan Babič
Citations: 131 • 2013
Computer Vision-Based Bridge Inspection and Monitoring: A Review
Kui Luo, Xuan Kong, Jie Zhang, Jiexuan Hu, Jinzhao Li, Hao Tang
Citations: 128 • 2023
Connecting Touch and Vision via Cross-Modal Prediction
Yunzhu Li, Jun-Yan Zhu, Russ Tedrake, Antonio Torralba
Citations: 126 • 2019