Vladislav I.

Responsible for conducting SoTA research across multiple directions, including

RL-based planning

• Worked closely with ML infra team to define and implement RL training infrastructure
• Developed core off-policy RL training algorithm used by RL planner
• Researched multiple approaches to integrate safety constraints into RL planning system

Generative approach to behavior prediction and Imitation Learning

• Did comprehensive analysis of generative modeling techniques as an alternative to regression-based Imitation Learning.
• Developed and implemented diffusion-based generative behavior prediction model.
• Generalized diffusion-based prediction model to provide interaction-aware joint prediction, showed superior performance on internal dataset for joint prediction.
• Developed novel diffusion distillation approach to reduce inference latency of diffusion-based predictor.

Anomaly detection for behavior models

• Implemented EBM-based anomaly detection model and showed superior performance compared to heuristic-based method used previously.

Responsible for deployment of Deep Learning models for robotic perception (including various computer vision and sensor fusion models)