Sriram Somasundaram

San Francisco, California, United States Contact Info
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Publications

  • Composing Complex Skills by Learning Transition Policies with Proximity Reward Induction

    International Conference on Learning Representations (ICLR)

    Intelligent creatures acquire complex skills by exploiting previously learned skills and learning to transition between them. To empower machines with this ability, we propose transition policies which effectively connect primitive skills to perform sequential tasks without handcrafted rewards. To effectively train our transition policies, we introduce proximity predictors which induce rewards gauging proximity to suitable initial states for the next skill. The proposed method is evaluated on a…

    Intelligent creatures acquire complex skills by exploiting previously learned skills and learning to transition between them. To empower machines with this ability, we propose transition policies which effectively connect primitive skills to perform sequential tasks without handcrafted rewards. To effectively train our transition policies, we introduce proximity predictors which induce rewards gauging proximity to suitable initial states for the next skill. The proposed method is evaluated on a diverse set of experiments for continuous control in both bi-pedal locomotion and robotic arm manipulation tasks in MuJoCo. We demonstrate that transition policies enable us to effectively learn complex tasks and the induced proximity reward computed using the initiation predictor improves training efficiency.

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  • Neural Program Synthesis from Diverse Demonstration Videos

    International Conference on Machine Learning (ICML)

    Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task…

    Interpreting decision making logic in demonstration videos is key to collaborating with and mimicking humans. To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos. We introduce a summarizer module as part of our model to improve the network’s ability to integrate multiple demonstrations varying in behavior. We also employ a multi-task objective to encourage the model to learn meaningful intermediate representations for end-to-end training. We show that our model is able to reliably synthesize underlying programs as well as capture diverse behaviors exhibited in demonstrations. Website and code at https://shaohua0116.github.io/demo2program.

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  • pH-susceptibility of HLA-DO tunes DO/DM ratios to regulate HLA-DM catalytic activity

    Scientific Reports (Nature)

    Other authors
    • Wei Jiang
    • Michael J. Strohman
    • Sashi Ayyangar
    • Tieying Hou
    • Nan Wang
    • Elizabeth Mellins
    See publication

Projects

  • Learning Car

    - Present

    Implemented reinforcement learning (RL) in a robotic car. Used the goBetwino API to connect an Arduino Mega microcontroller to RL program which received car state info from the serial port.

    Other creators
    See project

Honors & Awards

  • Summa Cum Laude

    University of Southern California

  • Additional Honors

    -

    Presidential Scholarship (USC)
    National AP Scholar - Computer Science, Calculus BC, Chemistry, Spanish, World History, U.S. History, Biology, Microeconomics, Macroeconomics, Psychology, Environmental Science, Statistics, Physics C: Mechanics, Physics C: Electricity and Magnetism
    National Merit Finalist
    2015 Intel STS Semifinalist
    Intel International Science and Engineering Fair (ISEF) - 2nd place (2014) in Biochemistry
    First place at Synopsys Championship Fair (2014) & Grand…

    Presidential Scholarship (USC)
    National AP Scholar - Computer Science, Calculus BC, Chemistry, Spanish, World History, U.S. History, Biology, Microeconomics, Macroeconomics, Psychology, Environmental Science, Statistics, Physics C: Mechanics, Physics C: Electricity and Magnetism
    National Merit Finalist
    2015 Intel STS Semifinalist
    Intel International Science and Engineering Fair (ISEF) - 2nd place (2014) in Biochemistry
    First place at Synopsys Championship Fair (2014) & Grand Prize – Best of Biological Sciences
    n+1 award at Synopsys

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