b'Fabio Ramos'

research

∙ 08/08/2023

Path Signatures for Diversity in Probabilistic Trajectory Optimisation

Motion planning can be cast as a trajectory optimisation problem where a...

0 Lucas Barcelos, et al. ∙

research

∙ 06/06/2023

Learning to Simulate Tree-Branch Dynamics for Manipulation

We propose to use a simulation driven inverse inference approach to mode...

0 Jayadeep Jacob, et al. ∙

research

∙ 05/26/2023

IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality

Robotic assembly is a longstanding challenge, requiring contact-rich int...

0 Bingjie Tang, et al. ∙

research

∙ 03/28/2023

DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets

Robotic grasping of 3D deformable objects is critical for real-world app...

0 Isabella Huang, et al. ∙

research

∙ 09/22/2022

Batch Bayesian optimisation via density-ratio estimation with guarantees

Bayesian optimisation (BO) algorithms have shown remarkable success in a...

0 Rafael Oliveira, et al. ∙

research

∙ 07/03/2022

Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skills

Deep reinforcement learning (RL) is a promising approach to solving comp...

0 Julia Tan, et al. ∙

research

∙ 04/14/2022

Accelerated Policy Learning with Parallel Differentiable Simulation

Deep reinforcement learning can generate complex control policies, but r...

0 Jie Xu, et al. ∙

research

∙ 03/19/2022

DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting

Robotic cutting of soft materials is critical for applications such as f...

6 Eric Heiden, et al. ∙

research

∙ 03/13/2022

Adaptive Model Predictive Control by Learning Classifiers

Stochastic model predictive control has been a successful and robust con...

9 Rel Guzman, et al. ∙

research

∙ 03/02/2022

L4KDE: Learning for KinoDynamic Tree Expansion

We present the Learning for KinoDynamic Tree Expansion (L4KDE) method fo...

0 Tin Lai, et al. ∙

research

∙ 03/01/2022

Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty

We propose an adaptive optimisation approach for tuning stochastic model...

0 Rel Guzman, et al. ∙

research

∙ 02/09/2022

Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation

Estimation of a dynamical system's latent state subject to sensor noise ...

0 Fahira Afzal Maken, et al. ∙

research

∙ 12/09/2021

A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation

Deformable object manipulation remains a challenging task in robotics re...

12 Rika Antonova, et al. ∙

research

∙ 11/01/2021

Robot Learning from Randomized Simulations: A Review

The rise of deep learning has caused a paradigm shift in robotics resear...

0 Fabio Muratore, et al. ∙

research

∙ 09/21/2021

Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph

In an environment where a manipulator needs to execute multiple pick-and...

0 Tin Lai, et al. ∙

research

∙ 09/18/2021

Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation

To accurately reproduce measurements from the real world, simulators nee...

0 Eric Heiden, et al. ∙

research

∙ 08/26/2021

Parallelised Diffeomorphic Sampling-based Motion Planning

We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PD...

0 Tin Lai, et al. ∙

research

∙ 07/09/2021

BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym

BayesSim is a statistical technique for domain randomization in reinforc...

0 Rika Antonova, et al. ∙

research

∙ 07/09/2021

Probabilistic Trajectory Prediction with Structural Constraints

This work addresses the problem of predicting the motion trajectories of...

0 Weiming Zhi, et al. ∙

research

∙ 07/04/2021

Learning ODEs via Diffeomorphisms for Fast and Robust Integration

Advances in differentiable numerical integrators have enabled the use of...

0 Weiming Zhi, et al. ∙

research

∙ 06/07/2021

Stein ICP for Uncertainty Estimation in Point Cloud Matching

Quantification of uncertainty in point cloud matching is critical in man...

0 Fahira Afzal Maken, et al. ∙

research

∙ 05/25/2021

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Robotic cutting of soft materials is critical for applications such as f...

0 Eric Heiden, et al. ∙

research

∙ 04/28/2021

Fast Joint Space Model-Predictive Control for Reactive Manipulation

Sampling-based model predictive control (MPC) is a promising tool for fe...

0 Mohak Bhardwaj, et al. ∙

research

∙ 03/23/2021

Dual Online Stein Variational Inference for Control and Dynamics

Model predictive control (MPC) schemes have a proven track record for de...

0 Lucas Barcelos, et al. ∙

research

∙ 02/17/2021

BORE: Bayesian Optimization by Density-Ratio Estimation

Bayesian optimization (BO) is among the most effective and widely-used b...

0 Louis C. Tiao, et al. ∙

research

∙ 11/17/2020

A User's Guide to Calibrating Robotics Simulators

Simulators are a critical component of modern robotics research. Strateg...

5 Bhairav Mehta, et al. ∙

research

∙ 11/16/2020

Fast Uncertainty Quantification for Deep Object Pose Estimation

Deep learning-based object pose estimators are often unreliable and over...

0 Guanya Shi, et al. ∙

research

∙ 11/15/2020

Stein Variational Model Predictive Control

Decision making under uncertainty is critical to real-world, autonomous ...

7 Alexander Lambert, et al. ∙

research

∙ 11/12/2020

Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements

Critical for the coexistence of humans and robots in dynamic environment...

0 Weiming Zhi, et al. ∙

research

∙ 11/05/2020

STReSSD: Sim-To-Real from Sound for Stochastic Dynamics

Sound is an information-rich medium that captures dynamic physical event...

0 Carolyn Matl, et al. ∙

research

∙ 10/21/2020

Learning to Plan Optimally with Flow-based Motion Planner

Sampling-based motion planning is the predominant paradigm in many real-...

0 Tin Lai, et al. ∙

research

∙ 10/12/2020

Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion

A critical decision process in data acquisition for mineral and energy r...

0 Sebastian Haan, et al. ∙

research

∙ 10/09/2020

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning

We establish a general form of explicit, input-dependent, measure-valued...

8 Anthony Tompkins, et al. ∙

research

∙ 10/01/2020

Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control

Model predictive control (MPC) has been successful in applications invol...

0 Rel Guzman, et al. ∙

research

∙ 07/01/2020

Online Domain Adaptation for Occupancy Mapping

Creating accurate spatial representations that take into account uncerta...

0 Anthony Tompkins, et al. ∙

research

∙ 05/27/2020

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems

Robotic tasks often require motions with complex geometric structures. W...

0 Muhammad Asif Rana, et al. ∙

research

∙ 05/21/2020

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning

Traditional robotic approaches rely on an accurate model of the environm...

33 Michelle A. Lee, et al. ∙

research

∙ 04/16/2020

Estimating Motion Uncertainty with Bayesian ICP

Accurate uncertainty estimation associated with the pose transformation ...

0 Fahira Afzal Maken, et al. ∙

research

∙ 04/06/2020

Intrinsic Exploration as Multi-Objective RL

Intrinsic motivation enables reinforcement learning (RL) agents to explo...

0 Philippe Morere, et al. ∙

research

∙ 03/18/2020

Inferring the Material Properties of Granular Media for Robotic Tasks

Granular media (e.g., cereal grains, plastic resin pellets, and pills) a...

0 Carolyn Matl, et al. ∙

research

∙ 02/18/2020

DISCO: Double Likelihood-free Inference Stochastic Control

Accurate simulation of complex physical systems enables the development,...

0 Lucas Barcelos, et al. ∙

research

∙ 01/20/2020

Reinforcement Learning with Probabilistically Complete Exploration

Balancing exploration and exploitation remains a key challenge in reinfo...

0 Philippe Morere, et al. ∙

research

∙ 12/09/2019

Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training

Magnetic Resonance Imaging (MRI) of the brain can come in the form of di...

14 Harrison Nguyen, et al. ∙

research

∙ 12/04/2019

Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment

This paper addresses the problem of learning instantaneous occupancy lev...

0 Vitor Guizilini, et al. ∙

research

∙ 11/20/2019

Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

Balancing exploration and exploitation is a fundamental part of reinforc...

0 Tom Blau, et al. ∙

research

∙ 11/13/2019

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

Learning from offline task demonstrations is a problem of great interest...

108 Ajay Mandlekar, et al. ∙

research

∙ 09/25/2019

OCTNet: Trajectory Generation in New Environments from Past Experiences

Being able to safely operate for extended periods of time in dynamic env...

0 Weiming Zhi, et al. ∙

research

∙ 09/08/2019

Local Sampling-based Planning with Sequential Bayesian Updates

Sampling-based planners are the predominant motion planning paradigm for...

0 Tin Lai, et al. ∙

research

∙ 09/05/2019

Occ-Traj120: Occupancy Maps with Associated Trajectories

Trajectory modelling had been the principal research area for understand...

0 Tin Lai, et al. ∙

research

∙ 07/22/2019

Speeding Up Iterative Closest Point Using Stochastic Gradient Descent

Sensors producing 3D point clouds such as 3D laser scanners and RGB-D ca...

0 Fahira Afzal Maken, et al. ∙

Fabio Ramos

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