Haresh Karnan

I am a PhD candidate at The University of Texas at Austin, working on Reinforcement Learning, Computer Vision and Artificial Intelligence for Robotics. I'm a member of the Learning Agents Research Group (LARG), advised by Peter Stone, and co-advised by Joydeep Biswas in the Computer Science department. I'm also a part of the UT Austin RoboCup@Home team.

I've previously interned at Amazon Scout where I worked on Vision based Robot Localization for their package delivery robot - Scout.

In my past life, I worked with Dr. Robert Skelton at Texas A&M University, College Station, on localization, sensing and control of Tensegrity robots.

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We introduce a large-scale, first-person-view dataset of socially compliant robot navigation demonstrations. SCAND consists of 138 trajectories, 25 miles of socially compliant navigation demonstrations collected on 2 robots by 4 human demonstrators within the UT Austin campus.

We introduce VI-IKD, a visual-inertial IKD model that learns to anticipate the kinodynamic interactions of the vehicle with the terrain ahead, enabling accurate high-speed navigation.

In this work, we introduce Optim-FKD, an approach for high-speed navigation that uses a learned forward kinodynamics model (FKD) coupled with non-linear least squares optimization for multi-horizon control.

Learning to imitate from an expert's video-only demonstration can be hard when there is significant viewpoint mismatch between the expert and the imitator agents. In this work, we propose VOILA, an IfO algorithm that imitates an expert driver's navigation policy from a single video-only demonstration, overcoming viewpoint mismatch.

SOTA approaches in Imitation from Video only demonstrations exhibit poor sample efficiency when learning with access to proprioceptive features of the imitator. To tackle this, we introduce VGAIfO-SO, an IfO algorithm that uses self-supervision to improve sample efficiency.

Sim-to-real is the problem of learning a control policy in an inaccurate simulated world such that the learned policy when transferred to the real-world, performs well. In this article, we explore the proposed black-box Sim-to-Real algorithm GAT, and its extension SGAT.

In this work, we propose the GARAT algorithm, which treats the Sim-to-Real problem as an Imitation from Observation (IfO) problem and uses advances in the IfO literature to transfer a control policy from a source domain to a target domain, using Adversarial Imitation Learning.

Real world dynamics are often stochastic and robot simulators have an inaccurate approximation of real world dynamics. In this work, we propose a Sim-to-Real algorithm called SGAT and transfer a Humanoid walk from Simulation to Real world.

In this work, we propose a Sim-to-Real algorithm called RGAT to ground an inaccurate simulator with data from the real world, using Reinforcement Learning.

Tensegrity mechanisms are known for their minimal-mass and flexible properties. In this work, we propose using vision based sensing for shape control of such soft robotic manipulators.

In this work, we propose the GARAT algorithm, which treats the Sim-to-Real problem as an Imitation from Observation (IfO) problem and uses advances in the IfO literature to transfer a control policy from a source domain to a target domain, using Adversarial Imitation Learning.

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