Search Results for author: Diego Romeres

Found 27 papers, 1 papers with code

Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection

no code implementations17 Dec 2023 Xinghao Zhu, Devesh K. Jha, Diego Romeres, Lingfeng Sun, Masayoshi Tomizuka, Anoop Cherian

Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling.

Motion Planning valid

A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification

no code implementations10 Oct 2023 Giulio Giacomuzzo, Alberto Dalla Libera, Diego Romeres, Ruggero Carli

First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system.

Gaussian Processes

Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos

no code implementations27 Jun 2023 Chiori Hori, Puyuan Peng, David Harwath, Xinyu Liu, Kei Ota, Siddarth Jain, Radu Corcodel, Devesh Jha, Diego Romeres, Jonathan Le Roux

This paper introduces a method for robot action sequence generation from instruction videos using (1) an audio-visual Transformer that converts audio-visual features and instruction speech to a sequence of robot actions called dynamic movement primitives (DMPs) and (2) style-transfer-based training that employs multi-task learning with video captioning and weakly-supervised learning with a semantic classifier to exploit unpaired video-action data.

Multi-Task Learning Scene Understanding +3

Learning Control from Raw Position Measurements

no code implementations30 Jan 2023 Fabio Amadio, Alberto Dalla Libera, Daniel Nikovski, Ruggero Carli, Diego Romeres

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.

Model-based Reinforcement Learning Position

Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control

no code implementations2 Dec 2022 Devesh K. Jha, Siddarth Jain, Diego Romeres, William Yerazunis, Daniel Nikovski

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.

Imitation Learning Pose Estimation

Active Exploration for Robotic Manipulation

no code implementations23 Oct 2022 Tim Schneider, Boris Belousov, Georgia Chalvatzaki, Diego Romeres, Devesh K. Jha, Jan Peters

Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years.

Model-based Reinforcement Learning Model Predictive Control

Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills

no code implementations28 Sep 2022 Seiji Shaw, Devesh K. Jha, Arvind Raghunathan, Radu Corcodel, Diego Romeres, George Konidaris, Daniel Nikovski

In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace.

Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation

no code implementations5 Mar 2022 Yuki Shirai, Devesh K. Jha, Arvind Raghunathan, Diego Romeres

In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics.

Imitation and Supervised Learning of Compliance for Robotic Assembly

no code implementations20 Nov 2021 Devesh K. Jha, Diego Romeres, William Yerazunis, Daniel Nikovski

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.

Industrial Robots Position

PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance

no code implementations6 Jun 2021 Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres

PYROBOCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs).

Collision Avoidance

Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models

no code implementations26 Apr 2021 Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski, Ruggero Carli, Diego Romeres

We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice.

Model-based Policy Search for Partially Measurable Systems

no code implementations21 Jan 2021 Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, Diego Romeres

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.

Gaussian Processes Model-based Reinforcement Learning +2

Understanding Multi-Modal Perception Using Behavioral Cloning for Peg-In-a-Hole Insertion Tasks

no code implementations22 Jul 2020 Yifang Liu, Diego Romeres, Devesh K. Jha, Daniel Nikovski

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.

Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements

no code implementations25 Feb 2020 Alberto Dalla Libera, Diego Romeres, Devesh K. Jha, Bill Yerazunis, Daniel Nikovski

In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR).

GPR Model-based Reinforcement Learning +2

Quasi-Newton Trust Region Policy Optimization

no code implementations26 Dec 2019 Devesh Jha, Arvind Raghunathan, Diego Romeres

The algorithm has achieved state-of-the-art performance when used in reinforcement learning across a wide range of tasks.

Continuous Control reinforcement-learning +1

Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control

2 code implementations23 Oct 2019 Jonathan Chang, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex

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.

Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

no code implementations13 Sep 2018 Jeroen van Baar, Alan Sullivan, Radu Cordorel, Devesh Jha, Diego Romeres, Daniel Nikovski

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.

Friction Transfer Learning

Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze

no code implementations13 Sep 2018 Diego Romeres, Devesh Jha, Alberto Dalla Libera, William Yerazunis, Daniel Nikovski

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.

Friction Gaussian Processes +1

Online semi-parametric learning for inverse dynamics modeling

no code implementations17 Mar 2016 Diego Romeres, Mattia Zorzi, Raffaello Camoriano, Alessandro Chiuso

This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model.

On-line Bayesian System Identification

no code implementations17 Jan 2016 Diego Romeres, Giulia Prando, Gianluigi Pillonetto, Alessandro Chiuso

We consider an on-line system identification setting, in which new data become available at given time steps.

Identification of stable models via nonparametric prediction error methods

no code implementations2 Jul 2015 Diego Romeres, Gianluigi Pillonetto, Alessandro Chiuso

Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system.

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