2 papers
cs.RO2026
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Jianing Qian, Qinhe Peng, Emmanuel Panov +4
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 t…
cs.RO2024
Continuously Improving Mobile Manipulation with Autonomous Real-World RL
Russell Mendonca, Emmanuel Panov, Bernadette Bucher +2
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)…