Search Results for author:
Found 7 papers, 1 papers with code
Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection
The model combines the motif of one graph, the core subgraph containing the identification (category) information, and the contextual subgraph (non-motif) of another graph to produce a raw counterfactual graph.
A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization
Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills.
iPLAN: Interactive and Procedural Layout Planning
However, the capability of involving humans into the loop has been largely ignored in existing methods which are mostly end-to-end approaches.
EKMP: Generalized Imitation Learning with Adaptation, Nonlinear Hard Constraints and Obstacle Avoidance
As a user-friendly and straightforward solution for robot trajectory generation, imitation learning has been viewed as a vital direction in the context of robot skill learning.
Imitation Learning
Motion Planning
Robotics
A Linearly Constrained Nonparametric Framework for Imitation Learning
Several examples including simulated writing and locomotion tasks are presented to show the effectiveness of our framework.
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
This rich set of information is used in combination with optimal controller fusion to learn actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task.
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space).
