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Niko Suenderhauf

13 accepted papers

2026

Changes in Real Time: Online Scene Change Detection with Multi-View Fusion

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Suenderhauf, Dimity Miller
CVPR 2026

Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained viewpoints. Existing online SCD methods are significantly less accurate than offline approaches. We present the first onli

Cited by 0Source ↗code ↗Scholar ↗
2025

IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation

Krishan Rana, Robert Lee, David Pershouse, Niko Suenderhauf
RSS 2025poster

Recent advances in imitation learning, particularly using generative modelling techniques like diffusion, have enabled policies to capture complex multi-modal action distributions. However, these methods often require large datasets and multiple inference steps for action generation, posing challeng…

Cited by 0PDF ↗Scholar ↗
2025

Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames

Krishan Rana, Jad Abou-Chakra, Sourav Garg, Robert Lee, Ian Reid, Niko Suenderhauf
CoRL 2025poster

Imitation learning has unlocked the potential for robots to exhibit highly dexterous behaviours. However, it still struggles with long-horizon, multi-object tasks due to poor sample efficiency and limited generalisation. Existing methods require a substantial number of demonstrations to cover possib…

Cited by 0Source ↗code ↗Scholar ↗
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee +273
ICRA 2024poster

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…

Cited by 259Source ↗code ↗Scholar ↗
2024

Open-Set Recognition in the Age of Vision-Language Models

Dimity Miller*, Niko Suenderhauf, Alex Kenna, Keita Mason
ECCV 2024poster

"Are vision-language models (VLMs) for open-vocabulary perception inherently open-set models because they are trained on internet-scale datasets? We answer this question with a clear no – VLMs introduce closed-set assumptions via their finite query set, making them vulnerable to open-set conditions.…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2024

Physically Embodied Gaussian Splatting: A Visually Learnt and Physically Grounded 3D Representation for Robotics

Jad Abou-Chakra, Krishan Rana, Feras Dayoub, Niko Suenderhauf
CoRL 2024poster

For robots to robustly understand and interact with the physical world, it is highly beneficial to have a comprehensive representation -- modelling geometry, physics, and visual observations -- that informs perception, planning, and control algorithms. We propose a novel dual "Gaussian-Particle" re…

Cited by 9Source ↗code ↗Scholar ↗
2023

SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, Niko Suenderhauf
CoRL 2023oral

Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a significant challenge for robotics. We introduce SayPlan, a scalable approach…

Cited by 315Source ↗code ↗Scholar ↗
2022

Forest Traversability Mapping (FTM): Traversability estimation using 3D voxel-based Normal Distributed Transform to enable forest navigation

Fabio Ruetz, Paulo Borges, Niko Suenderhauf, Emili Hernández, Thierry Peynot
IROS 2022poster

Autonomous navigation in dense vegetation remains an open challenge and is an area of major interest for the research community. In this paper we propose a novel traversability estimation method, the Forest Traversability Map, that gives autonomous ground vehicles the ability to navigate in harsh fo…

Cited by 9Source ↗Scholar ↗
2022

Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics

Krishan Rana, Ming Xu, Brendan Tidd, Michael Milford, Niko Suenderhauf
CoRL 2022poster

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert demonstrations and are embedded into a latent space from which they can be sampled as actions by a high-level RL agent. H…

Cited by 35Source ↗code ↗Scholar ↗
2019

Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

Sourav Garg, Madhu Babu V, Thanuja Dharmasiri, Stephen Hausler, Niko Suenderhauf, Swagat Kumar +2
ICRA 2019poster

Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change. Particularly challenging is the scenario where both phenomena occur simultaneously, such as when returning for the first time…

Cited by 29Source ↗code ↗Scholar ↗
2018

Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

Sourav Garg, Niko Suenderhauf, Michael Milford
ICRA 2018poster

When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typicall…

Cited by 94Source ↗Scholar ↗
2018

LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

Sourav Garg, Niko Suenderhauf, Michael Milford
RSS 2018poster

Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can rec…

Cited by 160Source ↗PDF ↗code ↗Scholar ↗
2015

Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free

Niko Suenderhauf, Sareh Shirazi, Adam Jacobson, Feras Dayoub, Edward Pepperell, Ben Upcroft +1
RSS 2015poster

Place recognition has long been an incompletely solved problem in that all approaches involve significant com- promises. Current methods address many but never all of the critical challenges of place recognition _ viewpoint-invariance, condition-invariance and minimizing training requirements. Here…

Cited by 503Source ↗PDF ↗Scholar ↗