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 reinforcement learning
- Generative Design
- Distributed learning
- Multi-agent systems
- Sim2real
- Robotic
Every day, we engage in the following activities:
- Creating environments in various simulations, including Pybullet, MuJoCo XLA (MJX), and Nvidia Isaac
- Innovate novel techniques to enhance reinforcement learning model performance
- Train 100s of experiment per day
- Compare experiments side by side and see the impact of different approaches on our results
