no code implementations • 26 Sep 2024 • Gabriel Sanfins, Fabio Ramos, Danilo Naiff
In this work, we introduce a neural network algorithm designed to automatically identify similarity relations from data.
1 code implementation • 1 Jun 2024 • Houston Warren, Rafael Oliveira, Fabio Ramos
In large-scale regression problems, random Fourier features (RFFs) have significantly enhanced the computational scalability and flexibility of Gaussian processes (GPs) by defining kernels through their spectral density, from which a finite set of Monte Carlo samples can be used to form an approximate low-rank GP.
no code implementations • 9 Dec 2023 • Motoya Ohnishi, Iretiayo Akinola, Jie Xu, Ajay Mandlekar, Fabio Ramos
As a specific case of our framework, we devise a model predictive control method for path tracking.
no code implementations • 3 Oct 2023 • Yewon Lee, Andrew Z. Li, Philip Huang, Eric Heiden, Krishna Murthy Jatavallabhula, Fabian Damken, Kevin Smith, Derek Nowrouzezahrai, Fabio Ramos, Florian Shkurti
We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes the hybrid optimization problem into a continuous domain.
no code implementations • 8 Aug 2023 • Lucas Barcelos, Tin Lai, Rafael Oliveira, Paulo Borges, Fabio Ramos
Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated.
no code implementations • 6 Jun 2023 • Jayadeep Jacob, Tirthankar Bandyopadhyay, Jason Williams, Paulo Borges, Fabio Ramos
We propose to use a simulation driven inverse inference approach to model the dynamics of tree branches under manipulation.
1 code implementation • 22 Sep 2022 • Rafael Oliveira, Louis Tiao, Fabio Ramos
Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions.
1 code implementation • 3 Jul 2022 • Julia Tan, Ransalu Senanayake, Fabio Ramos
Deep reinforcement learning (RL) is a promising approach to solving complex robotics problems.
1 code implementation • ICLR 2022 • Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin
In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness.
no code implementations • 19 Mar 2022 • Eric Heiden, Miles Macklin, Yashraj Narang, Dieter Fox, Animesh Garg, Fabio Ramos
In this work, we present DiSECt: the first differentiable simulator for cutting soft materials.
no code implementations • 13 Mar 2022 • Rel Guzman, Rafael Oliveira, Fabio Ramos
Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances.
no code implementations • 2 Mar 2022 • Tin Lai, Weiming Zhi, Tucker Hermans, Fabio Ramos
We study the kinodynamic variants of tree-based planning, where we have known system dynamics and kinematic constraints.
no code implementations • 1 Mar 2022 • Rel Guzman, Rafael Oliveira, Fabio Ramos
We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards.
no code implementations • 9 Dec 2021 • Rika Antonova, Jingyun Yang, Priya Sundaresan, Dieter Fox, Fabio Ramos, Jeannette Bohg
Deformable object manipulation remains a challenging task in robotics research.
no code implementations • 1 Nov 2021 • Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.
no code implementations • 29 Sep 2021 • Melissa Mozifian, Dieter Fox, David Meger, Fabio Ramos, Animesh Garg
In this paper, we consider the problem of continuous control for various robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks, related through the reward function.
no code implementations • 29 Sep 2021 • Weiming Zhi, Tin Lai, Lionel Ott, Edwin V Bonilla, Fabio Ramos
Consequently, by restricting the base ODE to be amenable to integration, we can speed up and improve the robustness of integrating trajectories from the learned system.
no code implementations • 18 Sep 2021 • Eric Heiden, Christopher E. Denniston, David Millard, Fabio Ramos, Gaurav S. Sukhatme
We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements.
no code implementations • 26 Aug 2021 • Tin Lai, Weiming Zhi, Tucker Hermans, Fabio Ramos
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP).
1 code implementation • 9 Jul 2021 • Rika Antonova, Fabio Ramos, Rafael Possas, Dieter Fox
This paper outlines BayesSimIG: a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym.
no code implementations • 9 Jul 2021 • Weiming Zhi, Lionel Ott, Fabio Ramos
This distribution is then used as a prior to a constrained optimisation problem which enforces chance constraints on the trajectory distribution.
no code implementations • 4 Jul 2021 • Weiming Zhi, Tin Lai, Lionel Ott, Edwin V. Bonilla, Fabio Ramos
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs).
2 code implementations • 7 Jun 2021 • Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Quantification of uncertainty in point cloud matching is critical in many tasks such as pose estimation, sensor fusion, and grasping.
1 code implementation • 17 Feb 2021 • Louis C. Tiao, Aaron Klein, Matthias Seeger, Edwin V. Bonilla, Cedric Archambeau, Fabio Ramos
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods.
no code implementations • 18 Jan 2021 • Caio Felippe Curitiba Marcellos, Gerson Francisco da Silva Junior, Elvis do Amaral Soares, Fabio Ramos, Amaro G. Barreto Jr
In several industrial applications, such as crystallization, pollution control, and flow assurance, an accurate understanding of the aqueous electrolyte solutions is crucial.
Chemical Physics
1 code implementation • 17 Nov 2020 • Bhairav Mehta, Ankur Handa, Dieter Fox, Fabio Ramos
Simulators are a critical component of modern robotics research.
no code implementations • 16 Nov 2020 • Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer.
no code implementations • 15 Nov 2020 • Alexander Lambert, Adam Fishman, Dieter Fox, Byron Boots, Fabio Ramos
By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem.
no code implementations • 12 Nov 2020 • Weiming Zhi, Tin Lai, Lionel Ott, Fabio Ramos
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements.
no code implementations • 21 Oct 2020 • Tin Lai, Fabio Ramos
The normalising flow based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based planners.
1 code implementation • 12 Oct 2020 • Sebastian Haan, Fabio Ramos, Dietmar Müller
A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost.
no code implementations • NeurIPS 2020 • Anthony Tompkins, Rafael Oliveira, Fabio Ramos
The resulting method is based on sparse spectrum Gaussian processes, enabling closed-form solutions, and is extensible to a stacked construction to capture more complex patterns.
no code implementations • 1 Oct 2020 • Rel Guzman, Rafael Oliveira, Fabio Ramos
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems.
1 code implementation • 1 Jul 2020 • Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.
2 code implementations • L4DC 2020 • Muhammad Asif Rana, Anqi Li, Dieter Fox, Byron Boots, Fabio Ramos, Nathan Ratliff
The complex motions are encoded as rollouts of a stable dynamical system, which, under a change of coordinates defined by a diffeomorphism, is equivalent to a simple, hand-specified dynamical system.
no code implementations • 21 May 2020 • Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.
no code implementations • 6 Apr 2020 • Philippe Morere, Fabio Ramos
To overcome this problem, we propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives.
1 code implementation • 18 Feb 2020 • Lucas Barcelos, Rafael Oliveira, Rafael Possas, Lionel Ott, Fabio Ramos
Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems.
no code implementations • 20 Jan 2020 • Philippe Morere, Gilad Francis, Tom Blau, Fabio Ramos
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL).
no code implementations • 9 Dec 2019 • Harrison Nguyen, Simon Luo, Fabio Ramos
On the other hand, there is smaller fraction of examples that contain all modalities (\emph{paired} data) and furthermore each modality is high dimensional when compared to number of datapoints.
no code implementations • 4 Dec 2019 • Vitor Guizilini, Ransalu Senanayake, Fabio Ramos
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.
1 code implementation • 20 Nov 2019 • Tom Blau, Lionel Ott, Fabio Ramos
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy.
no code implementations • 13 Nov 2019 • Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox
For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task.
no code implementations • pproximateinference AABI Symposium 2019 • Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
Parameters are one of the most critical components of machine learning models.
1 code implementation • pproximateinference AABI Symposium 2019 • Rafael Oliveira, Lionel Ott, Fabio Ramos
Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations.
no code implementations • 25 Sep 2019 • Weiming Zhi, Tin Lai, Lionel Ott, Gilad Francis, Fabio Ramos
This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.
no code implementations • 8 Sep 2019 • Tin Lai, Philippe Morere, Fabio Ramos, Gilad Francis
In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution.
no code implementations • 5 Sep 2019 • Tin Lai, Weiming Zhi, Fabio Ramos
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour.
1 code implementation • 22 Jul 2019 • Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation.
no code implementations • 11 Jul 2019 • Weiming Zhi, Lionel Ott, Fabio Ramos
Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments.
1 code implementation • 18 Jun 2019 • Philippe Morere, Lionel Ott, Fabio Ramos
Our framework decomposes transition dynamics into skill effects and success conditions, which allows fast planning by reasoning on effects, while learning conditions from interactions with the world.
1 code implementation • 4 Jun 2019 • Fabio Ramos, Rafael Carvalhaes Possas, Dieter Fox
We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator.
no code implementations • 1 Jun 2019 • Kelvin Hsu, Fabio Ramos
Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models.
no code implementations • 3 Mar 2019 • Kelvin Hsu, Fabio Ramos
In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations.
no code implementations • 21 Feb 2019 • Rafael Oliveira, Lionel Ott, Fabio Ramos
In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain.
1 code implementation • 1 Sep 2018 • Kelvin Hsu, Richard Nock, Fabio Ramos
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space.
no code implementations • 5 Jun 2018 • Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain.
no code implementations • CVPR 2018 • Rafael Possas, Sheila Pinto Caceres, Fabio Ramos
Recent advances in embedded technology have enabled more pervasive machine learning.
no code implementations • 14 May 2018 • Anthony Tompkins, Fabio Ramos
Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as in textures, crystallography, and quantum mechanics.
no code implementations • 27 Apr 2018 • Sahil Garg, Amarjeet Singh, Fabio Ramos
One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena.
no code implementations • 26 Apr 2018 • Sahil Garg, Amarjeet Singh, Fabio Ramos
The core idea in LISAL is to learn two models using Gaussian processes (GPs) wherein the first is a nonstationary GP directly modeling the phenomenon.
no code implementations • 26 Mar 2018 • Harrison Nguyen, Richard W. Morris, Anthony W. Harris, Mayuresh S. Korgoankar, Fabio Ramos
Here we transform T1-weighted brain images collected from two different sites into MR images from the same site.
no code implementations • 17 Feb 2018 • Rafael Oliveira, Fernando H. M. Rocha, Lionel Ott, Vitor Guizilini, Fabio Ramos, Valdir Grassi Jr
On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system.
no code implementations • ICLR 2018 • Vitor Guizilini, Fabio Ramos
This paper introduces the concept of continuous convolution to neural networks and deep learning applications in general.
no code implementations • 14 Dec 2017 • Thushan Ganegedara, Lionel Ott, Fabio Ramos
Adaptability is central to autonomy.
no code implementations • 7 Sep 2017 • Rafael Oliveira, Lionel Ott, Vitor Guizilini, Fabio Ramos
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform.
no code implementations • 7 Jan 2017 • Charika De Alvis, Lionel Ott, Fabio Ramos
The proposed method is evaluated on images and 3D point cloud data gathered in urban environments where image data provides the appearance features needed by the CRF, while the 3D point cloud data provides global spatial constraints over sets of nodes.
no code implementations • 8 Aug 2016 • Thushan Ganegedara, Lionel Ott, Fabio Ramos
As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning.
55 code implementations • 2 Feb 2016 • Alex Bewley, ZongYuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
Ranked #2 on
Multi-Object Tracking
on MOT15
1 code implementation • 25 Jan 2016 • Markus Schneider, Wolfgang Ertel, Fabio Ramos
We present a novel algorithm for anomaly detection on very large datasets and data streams.
no code implementations • 2 Jun 2014 • Markus Schneider, Fabio Ramos
We tackle the problem of multi-task learning with copula process.
no code implementations • 6 Mar 2014 • Lionel Ott, Linsey Pang, Fabio Ramos, David Howe, Sanjay Chawla
We present and contrast three relaxations to the integer program formulation: (i) a linear programming formulation (LP) (ii) an extension of affinity propagation to outlier detection (APOC) and (iii) a Lagrangian duality based formulation (LD).