Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Kurenkov, Andrey"'
Autor:
Lingelbach, Michael, Li, Chengshu, Hwang, Minjune, Kurenkov, Andrey, Lou, Alan, Martín-Martín, Roberto, Zhang, Ruohan, Fei-Fei, Li, Wu, Jiajun
Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relational object navigation (HRON), where the goal is to find objects specifie
Externí odkaz:
http://arxiv.org/abs/2306.13760
Autor:
Kurenkov, Andrey, Lingelbach, Michael, Agarwal, Tanmay, Jin, Emily, Li, Chengshu, Zhang, Ruohan, Fei-Fei, Li, Wu, Jiajun, Savarese, Silvio, Martín-Martín, Roberto
Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial information. We pose this as a new type of link prediction problem: link predic
Externí odkaz:
http://arxiv.org/abs/2305.17537
Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation. We developed a system that will automatically find the answer or explanation
Externí odkaz:
http://arxiv.org/abs/2212.08196
Autor:
Wong, Josiah, Tung, Albert, Kurenkov, Andrey, Mandlekar, Ajay, Fei-Fei, Li, Savarese, Silvio, Martín-Martín, Roberto
In mobile manipulation (MM), robots can both navigate within and interact with their environment and are thus able to complete many more tasks than robots only capable of navigation or manipulation. In this work, we explore how to apply imitation lea
Externí odkaz:
http://arxiv.org/abs/2112.05251
Autor:
Li, Chengshu, Xia, Fei, Martín-Martín, Roberto, Lingelbach, Michael, Srivastava, Sanjana, Shen, Bokui, Vainio, Kent, Gokmen, Cem, Dharan, Gokul, Jain, Tanish, Kurenkov, Andrey, Liu, C. Karen, Gweon, Hyowon, Wu, Jiajun, Fei-Fei, Li, Savarese, Silvio
Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simu
Externí odkaz:
http://arxiv.org/abs/2108.03272
Searching for objects in indoor organized environments such as homes or offices is part of our everyday activities. When looking for a target object, we jointly reason about the rooms and containers the object is likely to be in; the same type of con
Externí odkaz:
http://arxiv.org/abs/2012.04060
Autor:
Kurenkov, Andrey, Taglic, Joseph, Kulkarni, Rohun, Dominguez-Kuhne, Marcus, Garg, Animesh, Martín-Martín, Roberto, Savarese, Silvio
When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the object of interest and make it graspable. Due to the complexity of the
Externí odkaz:
http://arxiv.org/abs/2008.06073
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to levera
Externí odkaz:
http://arxiv.org/abs/1909.04121
Autor:
Danielczuk, Michael, Kurenkov, Andrey, Balakrishna, Ashwin, Matl, Matthew, Wang, David, Martín-Martín, Roberto, Garg, Animesh, Savarese, Silvio, Goldberg, Ken
When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects from cluttered bins, shelves, or tables. Mechanical Search describes t
Externí odkaz:
http://arxiv.org/abs/1903.01588
Autor:
Fang, Kuan, Zhu, Yuke, Garg, Animesh, Kurenkov, Andrey, Mehta, Viraj, Fei-Fei, Li, Savarese, Silvio
Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimiz
Externí odkaz:
http://arxiv.org/abs/1806.09266