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Found 3 papers, 1 papers with code
Object and Relation Centric Representations for Push Effect Prediction
Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene.
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL).
Belief Regulated Dual Propagation Nets for Learning Action Effects on Articulated Multi-Part Objects
In this paper, we introduce Belief Regulated Dual Propagation Networks (BRDPN), a general purpose learnable physics engine, which enables a robot to predict the effects of its actions in scenes containing groups of articulated multi-part objects.
Robotics
