no code implementations • 20 Sep 2023 • Junette Hsin, Shubhankar Agarwal, Adam Thorpe, Luis Sentis, David Fridovich-Keil
To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations.
no code implementations • 2 Mar 2023 • Kyle Morgenstein, Junfeng Jiao, Luis Sentis
This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments.
no code implementations • 13 Oct 2022 • Jee-eun Lee, Jaemin Lee, Tirthankar Bandyopadhyay, Luis Sentis
Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control.
1 code implementation • 19 Sep 2022 • Mingyo Seo, Ryan Gupta, Yifeng Zhu, Alexy Skoutnev, Luis Sentis, Yuke Zhu
We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands.
no code implementations • 20 Nov 2020 • Junhyeok Ahn, Luis Sentis
As an alternative, gray-box modeling leverages prior knowledge in neural network training but only for simple systems.
no code implementations • 23 Feb 2020 • Miguel Arduengo, Adrià Colomé, Joan Lobo-Prat, Luis Sentis, Carme Torras
Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks.
Robotics
no code implementations • 14 Sep 2019 • Rishi Shah, Yuqian Jiang, Haresh Karnan, Gilberto Briscoe-Martinez, Dominick Mulder, Ryan Gupta, Rachel Schlossman, Marika Murphy, Justin W. Hart, Luis Sentis, Peter Stone
RoboCup@Home is an international robotics competition based on domestic tasks requiring autonomous capabilities pertaining to a large variety of AI technologies.
1 code implementation • 25 Feb 2019 • Miguel Arduengo, Carme Torras, Luis Sentis
In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations.
Robotics