UMPNet: Universal Manipulation Policy Network for Articulated Objects
This work addresses the challenge of robotic manipulation for articulated objects, which is incremental as it builds on existing policy networks with novel attributes.
The authors tackled the problem of manipulating arbitrary articulated objects by introducing UMPNet, a single image-based policy network that infers closed-loop action sequences, achieving generalization to unseen objects without human demonstrations.
We introduce the Universal Manipulation Policy Network (UMPNet) -- a single image-based policy network that infers closed-loop action sequences for manipulating arbitrary articulated objects. To infer a wide range of action trajectories, the policy supports 6DoF action representation and varying trajectory length. To handle a diverse set of objects, the policy learns from objects with different articulation structures and generalizes to unseen objects or categories. The policy is trained with self-guided exploration without any human demonstrations, scripted policy, or pre-defined goal conditions. To support effective multi-step interaction, we introduce a novel Arrow-of-Time action attribute that indicates whether an action will change the object state back to the past or forward into the future. With the Arrow-of-Time inference at each interaction step, the learned policy is able to select actions that consistently lead towards or away from a given state, thereby, enabling both effective state exploration and goal-conditioned manipulation. Video is available at https://youtu.be/KqlvcL9RqKM