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Found 6 papers, 1 papers with code
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.
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design.
Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track.
Robotics Systems and Control Systems and Control Dynamical Systems Optimization and Control Applications
Learning from demonstration using products of experts: applications to manipulation and task prioritization
In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover tasks that are masked by the resolution of higher-level objectives.
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).
