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Reactive Reinforcement learning for Robotic Manipulation

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dc.contributor.advisor Camarasa, Gerardo-Aragon en_US
dc.contributor.author PORE, AMEYA en_US
dc.date.accessioned 2019-05-06T08:17:17Z
dc.date.available 2019-05-06T08:17:17Z
dc.date.issued 2019-05 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/2914
dc.description.abstract Robots have transformed the manufacturing industry and have been used for scientific exploration in human inaccessible environments like distant planets, Oceans, etc. However, a major barrier in its universal adoption is its lack of fragility and robustness in a complex and highly-diverse environment. This project constitutes the initial steps towards flexibility regarding exploration strategies that can be applied to challenging problems for autonomous grasping of rigid and deformable objects. Here, we employ recent advances in Deep Reinforcement learning (RL) to generate simple reactive behaviours like approaching, manipulating and retracting to pick an object. Once such simple behaviours are learnt, these could be sequenced in various combinations to give rise to a complex task. RL is a trial and error optimisation technique where an agent ought to take action in an environment to maximise some notion of cumulative reward. Current research in RL has been formulated on traditional techniques such as Deep Q-learning and policy gradient methods. These methods have worked well when the feedback/reward is dense. Perhaps, in real-life scenarios, the feedback is sparse, and these methods tend to fail in finding the optimum solutions and exploring the environment robustly. In this work, we have implemented two different approaches to solve such sparse reward problem, namely Curiosity and Reactive behaviour repertoire for long time step tasks. Our results have shown an immense reduction in training steps required to reach the maximum reward state in high-dimensional continuous action space compared to the baselines. en_US
dc.description.sponsorship Erasmus+ en_US
dc.language.iso en en_US
dc.subject 2019
dc.subject Artificial Intelligence en_US
dc.subject Machine learning en_US
dc.subject Reinforcement learning en_US
dc.subject Robotics en_US
dc.subject Computer vision en_US
dc.subject Computing Science en_US
dc.subject Behaviour based robotics en_US
dc.title Reactive Reinforcement learning for Robotic Manipulation en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Interdisciplinary en_US
dc.contributor.registration 20141119 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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