We would like to develop complex automation workflows that involve humans interacting with machines safely. To do so our systems have to learn in a more detailed manner. Using a mixture of input data from simulation as well as sensor data from real-world industry environments, we develop algorithms that learn like humans.

Research Interests:

• Deep reinforement learning
• Distributed learning
• Offline reinforcement learning
• Sim2real
• Robotic

Every day, we engage in the following activities:

• Creating environments in various simulations, including Pybullet and Nvidia Isaac
• Innovate novel techniques to enhance reinforcement learning model performance
• Train 100s of experiement per day
• Compare experiments side by side and see the impact of different approaches on our results