Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios.One of the key contributing factors to this progress is the scale of robot data used to train the models.To obtain large-scale datasets, prior approaches have relied on either demonstrations requiring high human involvement or engineering-heavy autonomous data collection schemes, both of which being challenging in scaling up the space of new tasks and skills needed for building generalist robots.To mitigate this issue, we propose to take an alternResearch goal: Does Reflexion's verbal reinforcement learning improve success rate on the ALFRED benchmark compared to behavioral cloning baselines when task instructions include novel object interactions?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.0/10.