Zobrazeno 1 - 10
of 5 114
pro vyhledávání: '"A. Martín Martín"'
Autor:
Wang, Zizhao, Hu, Jiaheng, Chuck, Caleb, Chen, Stephen, Martín-Martín, Roberto, Zhang, Amy, Niekum, Scott, Stone, Peter
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised skill discovery methods learn skills by encouraging distinguishable be
Externí odkaz:
http://arxiv.org/abs/2410.18416
Many robot manipulation tasks require active or interactive exploration behavior in order to be performed successfully. Such tasks are ubiquitous in embodied domains, where agents must actively search for the information necessary for each stage of a
Externí odkaz:
http://arxiv.org/abs/2410.18964
A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment. However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultane
Externí odkaz:
http://arxiv.org/abs/2410.11251
To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interacting with a wide and unseen range of everyday objects. We refer to these
Externí odkaz:
http://arxiv.org/abs/2410.06237
Autor:
Hsu, Cheng-Chun, Abbatematteo, Ben, Jiang, Zhenyu, Zhu, Yuke, Martín-Martín, Roberto, Biswas, Joydeep
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articulated objects, this study explores building scene-level articulation m
Externí odkaz:
http://arxiv.org/abs/2409.16473
Autor:
Hu, Jiaheng, Hendrix, Rose, Farhadi, Ali, Kembhavi, Aniruddha, Martin-Martin, Roberto, Stone, Peter, Zeng, Kuo-Hao, Ehsani, Kiana
In recent years, the Robotics field has initiated several efforts toward building generalist robot policies through large-scale multi-task Behavior Cloning. However, direct deployments of these policies have led to unsatisfactory performance, where t
Externí odkaz:
http://arxiv.org/abs/2409.16578
Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously collect observational data required to complete their survey task. Inspect
Externí odkaz:
http://arxiv.org/abs/2408.12513
Autor:
Tang, Chen, Abbatematteo, Ben, Hu, Jiaheng, Chandra, Rohan, Martín-Martín, Roberto, Stone, Peter
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated
Externí odkaz:
http://arxiv.org/abs/2408.03539
The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image space, learning a good visual representation requires a considerable am
Externí odkaz:
http://arxiv.org/abs/2405.10020
Autor:
Ge, Yunhao, Tang, Yihe, Xu, Jiashu, Gokmen, Cem, Li, Chengshu, Ai, Wensi, Martinez, Benjamin Jose, Aydin, Arman, Anvari, Mona, Chakravarthy, Ayush K, Yu, Hong-Xing, Wong, Josiah, Srivastava, Sanjana, Lee, Sharon, Zha, Shengxin, Itti, Laurent, Li, Yunzhu, Martín-Martín, Roberto, Liu, Miao, Zhang, Pengchuan, Zhang, Ruohan, Fei-Fei, Li, Wu, Jiajun
The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data gener
Externí odkaz:
http://arxiv.org/abs/2405.09546