Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Vemula, Anirudh"'
We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in the learned model, and the objective mismatch between model fitting an
Externí odkaz:
http://arxiv.org/abs/2303.00694
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior work showing its empirical effectiveness in applications such as chem
Externí odkaz:
http://arxiv.org/abs/2111.09434
Search-based techniques have shown great success in motion planning problems such as robotic navigation by discretizing the state space and precomputing motion primitives. However in domains with complex dynamic constraints, constructing motion primi
Externí odkaz:
http://arxiv.org/abs/2109.12427
Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator,
Externí odkaz:
http://arxiv.org/abs/2105.05019
Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especial
Externí odkaz:
http://arxiv.org/abs/2009.09942
Autor:
Vemula, Anirudh, Bagnell, J. Andrew
Trajectory optimization is an important tool for control and planning of complex, underactuated robots, and has shown impressive results in real world robotic tasks. However, in applications where the cost function to be optimized is non-smooth, mode
Externí odkaz:
http://arxiv.org/abs/2003.14393
Parameter space exploration methods with black-box optimization have recently been shown to outperform state-of-the-art approaches in continuous control reinforcement learning domains. In this paper, we examine reasons why these methods work better a
Externí odkaz:
http://arxiv.org/abs/2004.00500
Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots opera
Externí odkaz:
http://arxiv.org/abs/2003.04394
Autor:
Tallavajhula, Abhijeet, Schoisengeier, Adrian, Kim, Sung-Kyun, Vemula, Anirudh, Lister, Levi, Salzman, Oren
Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in better software validation and larger amounts of data. Previous efforts f
Externí odkaz:
http://arxiv.org/abs/1910.12284
Autor:
Islam, Fahad, Vemula, Anirudh, Kim, Sung-Kyun, Dornbush, Andrew, Salzman, Oren, Likhachev, Maxim
We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for a complex robotic system carrying two articulated mechanisms, an arm a
Externí odkaz:
http://arxiv.org/abs/1910.09453