Search Results for author:
Found 23 papers, 2 papers with code
Learning Control from Raw Position Measurements
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured.
Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control
In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans.
Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills
In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace.
Transformer Networks for Predictive Group Elevator Control
We propose a Predictive Group Elevator Scheduler by using predictive information of passengers arrivals from a Transformer based destination predictor and a linear regression model that predicts remaining time to destinations.
Imitation and Supervised Learning of Compliance for Robotic Assembly
This can be used to learn a suitable representation of the skill that can be generalized to novel positions of one of the parts involved in the assembly, for example the hole in a peg-in-hole (PiH) insertion task.
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice.
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient.
Model-based Policy Search for Partially Measurable Systems
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i. e., systems where the state can not be directly measured, but must be estimated through proper state observers.
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks
One of the main challenges in peg-in-a-hole (PiH) insertion tasks is in handling the uncertainty in the location of the target hole.
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency.
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR).
Multi-label Prediction in Time Series Data using Deep Neural Networks
In the most general setting of these types of problems, one or more samples of data across multiple time series can be assigned several concurrent fault labels from a finite, known set and the task is to predict the possibility of fault occurrence over a desired time horizon.
Local Policy Optimization for Trajectory-Centric Reinforcement Learning
Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem.
Model-based Reinforcement Learning
reinforcement-learning
+2
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
We demonstrate that our model trained on 33% of the possible goals is able to generalize to more than 90% of the targets in the scene for both simulation and robot experiments.
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.
Learning Dynamical Demand Response Model in Real-Time Pricing Program
In both cases, the dynamical DR model can be learned from historical price and energy consumption data.
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics
Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task---rather than being productive---can be reduced by transferring the learned task to the real robot.
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE).
Random Projection Filter Bank for Time Series Data
Different filters in RPFB extract different aspects of the time series, and together they provide a reasonably good summary of the time series.
Submodular Function Maximization for Group Elevator Scheduling
We show that this objective function is submodular.
