Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Rana, M. Asif"'
Autor:
Li, Anqi, Cheng, Ching-An, Rana, M. Asif, Xie, Man, Van Wyk, Karl, Ratliff, Nathan, Boots, Byron
We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class specified by RMPflow. RMPflow is a multi-task control framework that has been successfully applied in many robotics problems. U
Externí odkaz:
http://arxiv.org/abs/2103.05922
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-task problems through learning structured policies from human demonstra
Externí odkaz:
http://arxiv.org/abs/2012.13457
Autor:
Rana, M. Asif, Chen, Daphne, Ahmadzadeh, S. Reza, Williams, Jacob, Chu, Vivian, Chernova, Sonia
In this work, we contribute a large-scale study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of existing methods, it is critical that comprehensive empirical studies be p
Externí odkaz:
http://arxiv.org/abs/1911.02725
We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates tha
Externí odkaz:
http://arxiv.org/abs/1903.11725
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Generating robot motion that fulfills multiple tasks simultaneously is challenging due to the geometric constraints imposed by the robot. In this paper, we propose to solve multi-task problems through learning structured policies from human demonstra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d114fda45aea392e4f347a841f01814d
https://doi.org/10.1109/iros51168.2021.9636097
https://doi.org/10.1109/iros51168.2021.9636097
