GSL-PCD: Improving Generalist-Specialist Learning with Point Cloud Feature-based Task Partitioning

Generalization in Deep Reinforcement Learning across unseen environment variations often requires training over a diverse set of scenarios. However, random task partitioning in GSL can impede specialist performance, as it often assigns vastly different variations to the same specialist, typically resulting in each specialist being assigned just one variation, which increases computational costs. To improve this, we propose Generalist-Specialist Learning with Point Cloud Featurebased Task Partitioning (GSL-PCD). This approach clusters environment variations based on features extracted from object point clouds, using balanced clustering with a greedy algorithm to assign similar variations to the same specialist. Evaluations on robotic manipulation tasks from the ManiSkill benchmark demonstrate that point cloud feature-based partitioning outperforms vanilla partitioning by 9.4% with a fixed number of specialists and reduces computational and sample requirements by 50% to achieve comparable performance.