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Akshay L Chandra

PhD Student
Postal address:

Albert-Ludwigs-Universität Freiburg1
Technische Fakultät2
Robot Learning Lab3
Georges-Köhler-Allee 080
D-79110 Freiburg i. Br., Germany

Office: Building 80, 00-023
Email:    chandra-email
Phone: +49 761 203-8024
Website:   https://akshaychandra.com/4

About me

  • 05/2025 - current: Ph.D. Student at the Robot Learning Lab5, University of Freiburg
  • 10/2021 - 01/2025: M.Sc. in Computer Science, University of Freiburg
  • 12/2018 - 10/2021: Research Assistant, Machine Learning and Vision Lab6, IIT Hyderabad
  • 06/2017 - 09/2018: Software Engineer, GGK Technologies Pvt Ltd, Hyderabad
  • 08/2013 - 05/2017: B.Tech. in Computer Science, JNTU Hyderabad

Research Interests

  • Reinforcement Learning
  • World Models
  • Robot Manipulation

Publications

View my publications on Google Scholar7.

  • Iman Nematollahi, Branton DeMoss, Akshay L Chandra8Nick Hawes, Wolfram Burgard, Ingmar Posner
    LUMOS: Language-Conditioned Imitation Learning with World Models
    IEEE International Conference on Robotics and Automation, 2025
    arXiv9 website10 code11

    • Shivangana Rawat, Akshay L Chandra8, Sai Vikas Desai, Vineeth N Balasubramanian, Seishi Ninomiya, Wei Guo
      How Useful Is Image-Based Active Learning for Plant Organ Segmentation?
      Plant Phenomics, February 2022 [Impact Factor: 7.6]
      paper12
    • Akshay L Chandra*8, Sai Vikas Desai*, Chaitanya Devaguptapu*, Vineeth N. Balasubramanian
      On Initial Pools for Deep Active Learning
      Pre-registration in Machine Learning Workshop, NeurIPS & PMLR, December 2021
      paper13
    • Akshay L Chandra*8, Sai Vikas Desai*, Vineeth N Balasubramanian, Seishi Ninomiya, Wei. Guo
      Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops
      Plant Methods (BioMed Central), March 2020 [Impact Factor: 5.6]
      arXiv14
    • Sai Vikas Desai*Akshay L Chandra*8, Wei Guo, Seishi Ninomiya, Vineeth N Balasubramanian
      An Adaptive Supervision Framework for Active Learning in Object Detection
      British Machine Vision Conference, August 2019
      arXiv15