Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Lee, Gilwoo"'
Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding
Externí odkaz:
http://arxiv.org/abs/2002.11853
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as Bayesian Reinforcement Learning over latent Markov Decision Processes (MDPs). While Bayes-optimal
Externí odkaz:
http://arxiv.org/abs/2002.03042
We focus on the problem of designing an artificial agent (AI), capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible. Our insight
Externí odkaz:
http://arxiv.org/abs/1909.06527
Autor:
Feng, Ryan, Kim, Youngsun, Lee, Gilwoo, Gordon, Ethan K., Schmittle, Matt, Kumar, Shivaum, Bhattacharjee, Tapomayukh, Srinivasa, Siddhartha S.
A robot-assisted feeding system must successfully acquire many different food items. A key challenge is the wide variation in the physical properties of food, demanding diverse acquisition strategies that are also capable of adapting to previously un
Externí odkaz:
http://arxiv.org/abs/1906.02350
Autor:
Ke, Liyiming, Choudhury, Sanjiban, Barnes, Matt, Sun, Wen, Lee, Gilwoo, Srinivasa, Siddhartha
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL an
Externí odkaz:
http://arxiv.org/abs/1905.12888
We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP framework elegantly addresses model uncertainty by incorporating Bayesian
Externí odkaz:
http://arxiv.org/abs/1810.03048
Autor:
Lee, Gilwoo, Hou, Brian, Mandalika, Aditya, Lee, Jeongseok, Choudhury, Sanjiban, Srinivasa, Siddhartha S.
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribu
Externí odkaz:
http://arxiv.org/abs/1810.01014
Publikováno v:
IEEE Robotics and Automation Letters, Volume: 4 , Issue: 2 , Page(s): 1485 - 1492, April 2019
Autonomous feeding is challenging because it requires manipulation of food items with various compliance, sizes, and shapes. To understand how humans manipulate food items during feeding and to explore ways to adapt their strategies to robots, we col
Externí odkaz:
http://arxiv.org/abs/1804.08768
Autor:
Lee, Gilwoo
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
Externí odkaz:
http://hdl.handle.net/1721.1/100627
We present an algorithm for obtaining an optimal control policy for hybrid dynamical systems in cluttered environments. To the best of our knowledge, this is the first attempt to have a locally optimal solution for this specific problem setting. Our
Externí odkaz:
http://arxiv.org/abs/1710.05231