Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Sridhar, Ajay"'
The world is filled with a wide variety of objects. For robots to be useful, they need the ability to find arbitrary objects described by people. In this paper, we present LeLaN(Learning Language-conditioned Navigation policy), a novel approach that
Externí odkaz:
http://arxiv.org/abs/2410.03603
Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tun
Externí odkaz:
http://arxiv.org/abs/2403.00991
Robotic learning for navigation in unfamiliar environments needs to provide policies for both task-oriented navigation (i.e., reaching a goal that the robot has located), and task-agnostic exploration (i.e., searching for a goal in a novel setting).
Externí odkaz:
http://arxiv.org/abs/2310.07896
Autor:
Shah, Dhruv, Sridhar, Ajay, Dashora, Nitish, Stachowicz, Kyle, Black, Kevin, Hirose, Noriaki, Levine, Sergey
General-purpose pre-trained models ("foundation models") have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch
Externí odkaz:
http://arxiv.org/abs/2306.14846
Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effectiv
Externí odkaz:
http://arxiv.org/abs/2306.01874
Machine learning techniques rely on large and diverse datasets for generalization. Computer vision, natural language processing, and other applications can often reuse public datasets to train many different models. However, due to differences in phy
Externí odkaz:
http://arxiv.org/abs/2210.07450
Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could
Externí odkaz:
http://arxiv.org/abs/2210.03370
Autor:
Sridhar, Ajay
Publikováno v:
CMC Senior Theses.
A vast literature acknowledges that minority groups, particularly African-Americans, receive less, and lower-quality treatment than Caucasians in U.S. health facilities. It remains an open question as to how much of this disparity is a result of pove
Externí odkaz:
http://scholarship.claremont.edu/cmc_theses/247
http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1289&context=cmc_theses
http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1289&context=cmc_theses
Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79856d99e81315ce5c268d6d5752e05b