I am a PhD candidate at MIT CSAIL. I work with Prof. Julie Shah in the Interactive Robotics Group. My research interests include reinforcement learning, interactive machine learning, and human-robot interaction. My aim is to develop general intelligence AI systems that can adapt and interact with people. Towards this goal, we are developing more interpretable and interactive learning algorithms that can provide explanations for decisions and incorporate human feedback in various forms.

I received my M.S. at MIT in 2015. My work involved developing human-robot team training procedures. Prior to MIT, I received my B.S. in Computer Science from Georgia Tech.


Interpretable Transfer Learning

Developing intelligent agents that can interact in the real world requires agents to be adaptable to both situations and people. Generalizing to new situations requires successful transfer learning, or the adaptation of prior learned knowledge to new tasks. Adapting to people requires an interpretable medium for human-machine interaction. We are working on developing more interpretable transfer learning algorithms that can be incorporated more easily into interactive systems.
Learning Models for Human Preferences

As robots are integrated more into environments with people, it is essential for robots to adapt to people's preferences. In this work, we automatically learn user models from joint-action demostrations. We first learn human preferences using an unsupervised clustering algorithm and then use inverse reinforcement learning to learn a reward function for each preference. When working with a new user, the hidden preference is inferred online. We demonstrate through human subject experiments on a collaborative refinishing task that the framework supports effective human-robot teaming.
Human-Robot Team Training

With a rise in joint human-robot teams, an important concern is how we can effectively train these teams. "Perturbation training" is a training approach used in human teams in which team members practice variations of a task to generalize to new situations. In this work, we develop the first end-to-end framework for human-robot perturbation training, which includes a multi-agent transfer learning algorithm, a human-robot co-learning framework, and a communication protocol. We perform computational and human subject experiments to validate the benefits of our framework.


Journal and Conference Papers
Perturbation Training for Human-Robot Teams
Ramya Ramakrishnan, Chongjie Zhang, Julie Shah
Accepted in Journal of Artificial Intelligence Research (JAIR) 2017
Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks
Stefanos Nikolaidis, Ramya Ramakrishnan, Keren Gu, Julie Shah
Human-Robot Interaction (HRI) 2015
[Best Paper Award - Enabling Technologies]
Improved Human-Robot Team Performance through Cross-Training, A Human Team Training Approach
Stefanos Nikolaidis, Pem Lasota, Ramya Ramakrishnan, Julie Shah
International Journal of Robotics Research (IJRR) 2015

Workshop and Symposium Papers
Knowledge Transfer from a Human Perspective
Ramya Ramakrishnan, Julie Shah
AAMAS: Transfer in Reinforcement Learning Workshop 2017
Interpretable Transfer for Reinforcement Learning based on Object Similarities
Ramya Ramakrishnan, Karthik Narasimhan, Julie Shah
IJCAI: Interactive Machine Learning Workshop 2016
Towards Interpretable Explanations for Transfer Learning in Sequential Tasks
Ramya Ramakrishnan, Julie Shah
AAAI Symposium: Intelligent Systems for Supporting Distributed Human Teamwork 2016
From virtual to actual mobility: Assessing the benefits of active locomotion through an immersive virtual environment using a motorized wheelchair
Amelia Nybakke*, Ramya Ramakrishnan*, Victoria Interrante
IEEE Symposium on 3D User Interfaces (3DUI) 2012