Zobrazeno 1 - 10
of 145
pro vyhledávání: '"OTA, Kei"'
Autor:
Ota, Kei, Jha, Devesh K., Jain, Siddarth, Yerazunis, Bill, Corcodel, Radu, Shukla, Yash, Bronars, Antonia, Romeres, Diego
Imagine a robot that can assemble a functional product from the individual parts presented in any configuration to the robot. Designing such a robotic system is a complex problem which presents several open challenges. To bypass these challenges, the
Externí odkaz:
http://arxiv.org/abs/2406.05331
We present in-hand manipulation tasks where a robot moves an object in grasp, maintains its external contact mode with the environment, and adjusts its in-hand pose simultaneously. The proposed manipulation task leads to complex contact interactions
Externí odkaz:
http://arxiv.org/abs/2403.18960
Autor:
Ota, Kei, Jha, Devesh K., Jatavallabhula, Krishna Murthy, Kanezaki, Asako, Tenenbaum, Joshua B.
Precise perception of contact interactions is essential for fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other (see Fig.
Externí odkaz:
http://arxiv.org/abs/2309.14552
Autor:
Hori, Chiori, Peng, Puyuan, Harwath, David, Liu, Xinyu, Ota, Kei, Jain, Siddarth, Corcodel, Radu, Jha, Devesh, Romeres, Diego, Roux, Jonathan Le
To realize human-robot collaboration, robots need to execute actions for new tasks according to human instructions given finite prior knowledge. Human experts can share their knowledge of how to perform a task with a robot through multi-modal instruc
Externí odkaz:
http://arxiv.org/abs/2306.15644
Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. Our tactile sensing provides us with a lot of information regarding contact formations as well as geometric information about objects during any interaction. With thi
Externí odkaz:
http://arxiv.org/abs/2303.06034
Autor:
Ota, Kei, Tung, Hsiao-Yu, Smith, Kevin A., Cherian, Anoop, Marks, Tim K., Sullivan, Alan, Kanezaki, Asako, Tenenbaum, Joshua B.
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects with strategic trial-and-error: first pushing a door then pulling if t
Externí odkaz:
http://arxiv.org/abs/2210.12521
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes. To this end, we propose Object
Externí odkaz:
http://arxiv.org/abs/2203.14708
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require intensive sampling from the current policy for stable and optimal performa
Externí odkaz:
http://arxiv.org/abs/2109.04307
The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural networks with millions or billions of parameters which can then be trained with massive amounts of data.
Externí odkaz:
http://arxiv.org/abs/2102.07920
Autor:
Ota, Kei, Jha, Devesh K., Romeres, Diego, van Baar, Jeroen, Smith, Kevin A., Semitsu, Takayuki, Oiki, Tomoaki, Sullivan, Alan, Nikovski, Daniel, Tenenbaum, Joshua B.
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of sample
Externí odkaz:
http://arxiv.org/abs/2011.07193