Emmanuel Panov — accepted AI-conference papers

2026

Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs

Jianing Qian, Qinhe Peng, Emmanuel Panov, Leonor Fermoselle, Dinesh Jayaraman, Bernadette Bucher +1

CVPR 2026

Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots

Cited by 0 Source ↗Scholar ↗

2024

Continuously Improving Mobile Manipulation with Autonomous Real-World RL

Russell Mendonca, Emmanuel Panov, Bernadette Bucher, Jiuguang Wang, Deepak Pathak

CoRL 2024poster

We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1) task-relevant autonomy, which guides exploration towards object interactions and prevents stagnation near goal states, 2…

Cited by 3 Source ↗code ↗Scholar ↗