Workshop: Generalizable Policy Learning in the Physical World

Let’s Handle It: Generalizable Manipulation of Articulated Objects

Zhutian Yang · Aidan Curtis


In this project we present a framework for building generalizable manipulation controller policies that map from raw input point clouds and segmentation masks to joint velocities. We took a traditional robotics approach, using point cloud processing, end-effector trajectory calculation, inverse kinematics, closed-loop position controllers, and behavior trees. We demonstrate our framework on four manipulation skills on common household objects that comprise the SAPIEN ManiSkill Manipulation challenge.

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