Daniel Dugas — accepted AI-conference papers

2026

WorldPlanner: Monte Carlo Tree Search and MPC with Action-Conditioned Visual World Models

Rooholla Khorrambakht, Joaquim Ortiz-Haro, Joseph Amigo, Omar Mostafa, Daniel Dugas, Franziska Meier +1

ICRA 2026poster

Robots must understand their environment from raw sensory inputs and reason about the consequences of their actions in it to solve complex tasks. Behavior Cloning (BC) leverages task-specific human demonstrations to learn this knowledge as end-to-end policies. However, these policies are difficult t…

Cited by 0 Source ↗PDF ↗Scholar ↗

2025

LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Paul McVay, Sergio Arnaud, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas +16

ICML 2025spotlight

We present LOCATE 3D, a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp." LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities. Notably, LOCA…

Cited by 0 Source ↗PDF ↗Scholar ↗

2022

FlowBot: Flow-based Modeling for Robot Navigation

Daniel Dugas, Kuanqi Cai, Olov Andersson, Nicholas Lawrance, Roland Siegwart, Jen Jen Chung

IROS 2022poster

Autonomous navigation among people is a com-plex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-…

Cited by 4 Source ↗Scholar ↗

2022

NavDreams: Towards Camera-Only RL Navigation Among Humans

Daniel Dugas, Olov Andersson, Roland Siegwart, Jen Jen Chung

IROS 2022poster

Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While images present a significant challenge when it comes to pe…

Cited by 17 Source ↗code ↗Scholar ↗

2021

Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd

Fabien Grzeskowiak, David Gonon, Daniel Dugas, Diego Paez-Granados, Jen Jen Chung, Juan Nieto +4

ICRA 2021poster

The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a benchmark tool to evaluate such capabilities; our long term vision is to provide the community with a simulation tool that…

Cited by 23 Source ↗Scholar ↗

2021

NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung

ICRA 2021poster

Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent resear…

Cited by 71 Source ↗code ↗Scholar ↗

2020

IAN: Multi-Behavior Navigation Planning for Robots in Real, Crowded Environments

Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung

IROS 2020poster

State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unst…

Cited by 21 Source ↗Scholar ↗

2020

Robot Navigation in Crowded Environments Using Deep Reinforcement Learning

Lucia Liu, Daniel Dugas, Gianluca Cesari, Roland Siegwart, Renaud Dubé

IROS 2020poster

Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environ…

Cited by 142 Source ↗Scholar ↗

2018

SegMap: 3D Segment Mapping using Data-Driven Descriptors

RSS 2018poster

When performing localization and mapping, working at the level of structure can be advantageous in terms of robustness to environmental changes and differences in illumination. This paper presents SegMap: a map representation solution to the localization and mapping problem based on the extraction o…

Cited by 218 Source ↗PDF ↗code ↗Scholar ↗

2017

SegMatch: Segment based place recognition in 3D point clouds

Renaud Dubé, Daniel Dugas, Elena Stumm, Juan Nieto, Roland Siegwart, Cesar Cadena

ICRA 2017poster

Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch…

Cited by 418 Source ↗Scholar ↗