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Found 16 papers, 5 papers with code
SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
Tensor Train for Global Optimization Problems in Robotics
We treat the task parameters as random variables, and for a given task, we generate samples for decision variables from the conditional distribution to initialize the optimization solver.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system.
Imitation of Manipulation Skills Using Multiple Geometries
Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes.
Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series
This chapter presents an overview of techniques used for the analysis, edition, and synthesis of time series, with a particular emphasis on motion data.
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 Constrained Distributions of Robot Configurations with Generative Adversarial Network
We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints.
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.
Interaction-limited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training.
A memory of motion for visual predictive control tasks
This paper addresses the problem of efficiently achieving visual predictive control tasks.
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach.
Learning from demonstration with model-based Gaussian process
In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task.
Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control
This article presents an overview of robot learning and adaptive control applications that can benefit from a joint use of Riemannian geometry and probabilistic representations.
Tensor-variate Mixture of Experts for Proportional Myographic Control of a Robotic Hand
The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available.
A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot.
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).
