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Ben Burgess-Limerick

7 accepted papers

2024

Demonstrating Event-Triggered Investigation and Sample Collection for Human Scientists using Field Robots and Large Foundation Models

Tirthankar Bandyopadhyay, Fletcher Talbot, Callum Bennie, Hashini Senaratne, Xun Li, Brendan Tidd +17
RSS 2024poster

In this paper, we introduce a pioneering end-to-end system demonstrated on a team of robots and sensors, designed to augment scientific exploration and discovery for human scientists in remote or inaccessible environments. We demonstrate and analyse our system's capability in a mock-up test-bed scen…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

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

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

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 910Source ↗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

Reactive Base Control for On-the-Move Mobile Manipulation in Dynamic Environments

Ben Burgess-Limerick, Jesse Haviland, Chris Lehnert, Peter Corke
RA-L 2024

We present a reactive base control method that enables high performance mobile manipulation on-the-move in real-world environments with static and dynamic obstacles. Performing manipulation tasks while the mobile base remains in motion can significantly decrease the time required to perform multi-st

Cited by 24Source ↗Scholar ↗
2023

An Architecture for Reactive Mobile Manipulation On-The-Move

Ben Burgess-Limerick, Chris Lehnert, Jürgen Leitner, Peter Corke
ICRA 2023poster

We present a generalised architecture for reactive mobile manipulation while a robot's base is in motion toward the next objective in a high-level task. By performing tasks on-the-move, overall cycle time is reduced compared to methods where the base pauses during manipulation. Reactive control of t…

Cited by 25Source ↗code ↗Scholar ↗
2022

DGBench: An Open-Source, Reproducible Benchmark for Dynamic Grasping

Ben Burgess-Limerick, Chris Lehnert, Jürgen Leitner, Peter Corke
IROS 2022poster

This paper introduces DGBench, a fully reproducible open-source testing system to enable benchmarking of dynamic grasping in environments with unpredictable relative motion between robot and object. We use the proposed benchmark to compare several visual perception arrangements. Traditional percepti…

Cited by 11Source ↗Scholar ↗
2022

Visibility Maximization Controller for Robotic Manipulation

Kerry He, Rhys Newbury, Tin Tran, Jesse Haviland, Ben Burgess-Limerick, Dana Kulic +2
RA-L 2022

Occlusions caused by a robot’s own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive controller that minimizes self-occlusions while achieving a desired goal pose. The approach allows coordinated control between

Cited by 19Source ↗code ↗Scholar ↗