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Found 5 papers, 2 papers with code
Estimating Material Properties of Interacting Objects Using Sum-GP-UCB
Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately.
Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
Sketches are abstract representations of visual perception and visuospatial construction.
DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
We propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning.
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
