Trajectory

Related papers: 20

About

A trajectory in robotics and AI refers to the time-parameterized path that a robot, vehicle, or end-effector follows through space, specifying not just positions but also velocities, accelerations, and sometimes forces at each point in time. Unlike a geometric path, a trajectory explicitly encodes *when* and *how fast* a system moves between configurations. Trajectories are fundamental across nearly every robotic discipline: manipulator arms follow planned joint-space or Cartesian trajectories to perform precise tasks; mobile robots and autonomous vehicles use trajectory planning to navigate safely among obstacles; aerial robots like quadrotors execute aggressive maneuvers via dynamically feasible trajectory generation; and SLAM systems reconstruct a robot's trajectory through an environment for localization and mapping. Trajectory control and tracking—ensuring a robot follows a desired trajectory despite disturbances—is a central challenge addressed by techniques ranging from sliding mode control to adaptive and iterative learning methods. In machine learning contexts, trajectory data drives imitation learning and visuomotor policy training. Accurate trajectory generation, representation, and tracking are foundational to achieving reliable, safe, and efficient robot behavior in real-world applications.

Top Cited Papers

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Bettering operation of Robots by learning

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Randomized Kinodynamic Planning

Steven M. LaValle, James Kuffner

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Hybrid Position/Force Control of Manipulators

Marc H. Raibert, John Craig

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Motion Planning in Dynamic Environments Using Velocity Obstacles

Paolo Fiorini, Zvi Shiller

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J.-J.E. Slotine, Shankar Sastry

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A hierarchical neural-network model for control and learning of voluntary movement

Mitsuo Kawato, Kazunori Furukawa, Ryoji Suzuki

Citations: 1575 • 1987

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Johan Wessberg, Christopher R. Stambaugh, Jerald D. Kralik, Pamela D. Beck, Mark Laubach, John K. Chapin, Jung Kim, Sean Biggs, Mandayam A. Srinivasan, Miguel A. L. Nicolelis

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Argoverse: 3D Tracking and Forecasting With Rich Maps

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Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex

John K. Chapin, Karen A. Moxon, Ronald S. Markowitz, Miguel A. L. Nicolelis

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On Learning, Representing, and Generalizing a Task in a Humanoid Robot

Sylvain Calinon, F. Guenter, Aude Billard

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End-to-end driving via conditional imitation learning

Felipe Codevilla, Antonio M. López, Vladlen Koltun, Alexey Dosovitskiy

Citations: 1065

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Chao Yu, Zuxin Liu, Xin-Jun Liu, Fugui Xie, Yi Yang, Qi Wei

Citations: 1052 • 2018