Expert Intervention Learning

Autor: Sanjiban Choudhury, Mung Chiang, Peter J. Ramadge, Jonathan C. Spencer, Sidd Srinivasa, Matt Barnes, Matt Schmittle

Publikováno v: Autonomous Robots. 46:99-113

Scalable robot learning from human-robot interaction is critical if robots are to solve a multitude of tasks in the real world. Current approaches to imitation learning suffer from one of two drawbacks. On the one hand, they rely solely on off-policy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3c328597c6930caf67c8e76cce9f78f3
https://doi.org/10.1007/s10514-021-10006-9

The blindfolded traveler’s problem: A search framework for motion planning with contact estimates

Autor: Brad Saund, Sanjiban Choudhury, Siddhartha Srinivasa, Dmitry Berenson

Publikováno v: The International Journal of Robotics Research. :027836492311708

We address the problem of robot motion planning under uncertainty where the only observations are through contact with the environment. Such problems are typically solved by planning optimistically assuming unknown space is free, moving along the pla

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0e2437426c14cd043ee9c05a141bfb6e
https://doi.org/10.1177/02783649231170893

Leveraging Experience in Lazy Search

Autor: Byron Boots, Siddhartha S. Srinivasa, Mohak Bhardwaj, Sanjiban Choudhury

Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bf19fce3552389dbd2e76098a3ec8c1
http://arxiv.org/abs/2110.04669

Imitation Learning as f-Divergence Minimization

Autor: Sanjiban Choudhury, Gilwoo Lee, Wen Sun, Siddhartha S. Srinivasa, Liyiming Ke, Matt Barnes

Publikováno v: Algorithmic Foundations of Robotics XIV ISBN: 9783030667221
WAFR

We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d950e56ebd3dbdcfc972a6701a54bdc4
https://doi.org/10.1007/978-3-030-66723-8_19

Autonomous aerial cinematography in unstructured environments with learned artistic decision-making

Autor: Rogerio Bonatti, Erdal Kayacan, Aayush Ahuja, Efe Camci, Sanjiban Choudhury, Wenshan Wang, Sebastian Scherer, Cherie Ho, Mirko Gschwindt

Publikováno v: Bonatti, R, Wang, W, Ho, C, Ahuja, A, Gschwindt, M, Camci, E, Kayacan, E, Choudhury, S & Scherer, S 2020, ' Autonomous aerial cinematography in unstructured environments with learned artistic decision-making ', Journal of Field Robotics, vol. 37, no. 4, pp. 606-641 . https://doi.org/10.1002/rob.21931

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7790b8dd09c590ee356c387ba5ef809b
https://pure.au.dk/portal/da/publications/autonomous-aerial-cinematography-in-unstructured-environments-with-learned-artistic-decisionmaking(13921666-9785-4634-8fdc-c222152e57f4).html