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| team:feroz_ahmed_siddiky [2016/02/18 11:32] – frickeni | team:feroz_ahmed_siddiky [2022/07/14 11:15] – [Deep Action Obserever] siddiky | ||
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| - | |:::|Tel: |–49 -421 218 64000| | + | |:::|Tel: |–49 -421 218 64027| |
| |:::|Fax: |--49 -421 218 64047| | |:::|Fax: |--49 -421 218 64047| | ||
| |:::|Room: |TAB 1.81| | |:::|Room: |TAB 1.81| | ||
| |:::|Mail: |< | |:::|Mail: |< | ||
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| + | ==== Deep Action Obserever ===== | ||
| + | Robotic agents have to learn how to perform manipulation tasks. One of the biggest challenges in this context is that | ||
| + | manipulation actions are performed in a variety of ways depending on the objects that the robot acts on, the tools it is | ||
| + | using, the task context, as well as the scene the action is to be executed in. This raises the issue of when to perform | ||
| + | a manipulation action in which way. In this paper we propose to let the robot read text instructions and watch the | ||
| + | corresponding videos illustrating how the steps are performed in order to generate symbolic action descriptions from the | ||
| + | text instructions. The text instructions are disambiguated and completed with the information contained in the videos. The | ||
| + | resulting action descriptions are close to action descriptions that can be executed by leading-edge cognition-enabled robot | ||
| + | control plans. To perform this learning task we combine two of the most powerful learning and reasoning mechanisms: | ||
| + | Deep Learning and Markov Logic Networks. Convolutional networks parameterized through deep learning recognize | ||
| + | objects, hand poses, and estimate poses and motions while the Markov logic networks use joint probability over the | ||
| + | relational structure of instructions to fill in missing information and disambiguate descriptions. Besides the combination | ||
| + | of symbolic and sub-symbolic reasoning the novel contributions include a Multi Task Network developed in a single | ||
| + | framework, optimized for computational cost, which can process 10 frames per second. We evaluate our framework on a | ||
| + | large number of video clips and show its impressive ability to interpret the manipulation tasks. | ||
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| + | [1] Feroz Ahmed Siddiky and Michael Beetz, " | ||
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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