Search Results for author: Lionel Ott

Found 28 papers, 10 papers with code

Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields (NeRF)

no code implementations3 May 2022 Michael Pantic, Cesar Cadena, Roland Siegwart, Lionel Ott

This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning.

Motion Planning

Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses

1 code implementation19 Jan 2022 Kenneth Blomqvist, Jen Jen Chung, Lionel Ott, Roland Siegwart

In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.

Object Tracking Pose Estimation

Learning Efficient and Robust Ordinary Differential Equations via Diffeomorphisms

no code implementations29 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.

Probabilistic Trajectory Prediction with Structural Constraints

no code implementations9 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.

Trajectory Prediction

Learning ODEs via Diffeomorphisms for Fast and Robust Integration

no code implementations4 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).

Stein ICP for Uncertainty Estimation in Point Cloud Matching

2 code implementations7 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.

Autonomous Driving Decision Making +2

Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

no code implementations20 Jan 2021 Weixuan Zhang, Lionel Ott, Marco Tognon, Roland Siegwart, Juan Nieto

However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge.

Robotics Systems and Control Systems and Control

Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter

1 code implementation4 Jan 2021 Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, Juan Nieto

General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.

Robotics

Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements

no code implementations12 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.

Go Fetch: Mobile Manipulation in Unstructured Environments

no code implementations2 Apr 2020 Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian Förster, Margarita Grinvald, Florian Tschopp, Jen Jen Chung, Lionel Ott, Juan Nieto, Roland Siegwart

With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.

Motion Planning

DISCO: Double Likelihood-free Inference Stochastic Control

1 code implementation18 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.

Bayesian Curiosity for Efficient Exploration in Reinforcement Learning

1 code implementation20 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.

Efficient Exploration reinforcement-learning

Distributional Bayesian optimisation for variational inference on black-box simulators

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.

Bayesian Optimisation reinforcement-learning +1

OCTNet: Trajectory Generation in New Environments from Past Experiences

no code implementations25 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.

motion prediction

An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments

2 code implementations20 Sep 2019 Lukas Schmid, Michael Pantic, Raghav Khanna, Lionel Ott, Roland Siegwart, Juan Nieto

However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage.

3D Reconstruction

Speeding Up Iterative Closest Point Using Stochastic Gradient Descent

1 code implementation22 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.

Autonomous Driving Pose Estimation

Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction

no code implementations11 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.

motion prediction

Learning to Plan Hierarchically from Curriculum

1 code implementation18 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.

Bayesian optimisation under uncertain inputs

no code implementations21 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.

Bayesian Optimisation

Learning to Race through Coordinate Descent Bayesian Optimisation

no code implementations17 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.

Bayesian Optimisation Car Racing

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

no code implementations7 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.

Bayesian Optimisation

Urban Scene Segmentation with Laser-Constrained CRFs

no code implementations7 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.

Scene Segmentation

Online Adaptation of Deep Architectures with Reinforcement Learning

no code implementations8 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.

Denoising online learning +1

Simple Online and Realtime Tracking

50 code implementations2 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.

Multiple Object Tracking

On Integrated Clustering and Outlier Detection

no code implementations NeurIPS 2014 Lionel Ott, Linsey Pang, Fabio T. Ramos, Sanjay Chawla

We model the joint clustering and outlier detection problem using an extension of the facility location formulation.

Outlier Detection

Integer Programming Relaxations for Integrated Clustering and Outlier Detection

no code implementations6 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).

Outlier Detection

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