Learning to combine primitive skills: A step towards versatile robotic manipulation

Understand

Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision.

• Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes.
• Recent learning methods can operate directly on visual inputs but typically require many demonstrations and/or task-specific reward engineering.
• In this work we aim to overcome previous limitations and propose a reinforcement learning (RL) approach to task planning that learns to combine primitive skills.