AIConfPaper
← Search

Martin Engelcke

10 accepted papers

2022

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones +2
ICRA 2022poster

Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed styles, current planners are unable to vary key gait parameters continuously while the r…

Cited by 6Source ↗Scholar ↗
2022

Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones, Martin Engelcke, Ioannis Havoutis +1
RA-L 2022

We present a novelapproach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Opti

Cited by 17Source ↗Scholar ↗
2021

APEX: Unsupervised, Object-Centric Scene Segmentation and Tracking for Robot Manipulation

Yizhe Wu, Oiwi Parker Jones, Martin Engelcke, Ingmar Posner
IROS 2021poster

Recent advances in unsupervised learning for object detection, segmentation, and tracking hold significant promise for applications in robotics. A common approach is to frame these tasks as inference in probabilistic latent-variable models. In this paper, however, we show that the current state-of-t…

Cited by 23Source ↗Scholar ↗
2021

GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement

Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
NeurIPS 2021poster

Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These methods, however, are limited to simulated and real-world datasets with limited vis…

Cited by 129Source ↗PDF ↗code ↗Scholar ↗
2020

First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion

Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon +2
IROS 2020poster

Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an o…

Cited by 12Source ↗Scholar ↗
2020

GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations

Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, Ingmar Posner
ICLR 2020poster

Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recen…

Cited by 316Source ↗code ↗Scholar ↗
2020

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra +1
NeurIPS 2020poster

We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accou…

Cited by 60Source ↗PDF ↗code ↗Scholar ↗
2019

On the Limitations of Representing Functions on Sets

Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne
ICML 2019oral

Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance. In particular, it has been conjectured that the dimension of this latent space may remain fixed as the cardinality of the sets under consideration increases…

Cited by 229Source ↗PDF ↗Scholar ↗
2018

3D Semantic Segmentation With Submanifold Sparse Convolutional Networks

Benjamin Graham, Martin Engelcke, Laurens van der Maaten
CVPR 2018poster

Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D point clouds that were obtained using a LiDAR scan…

Cited by 1932Source ↗PDF ↗code ↗Scholar ↗
2017

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner
ICRA 2017poster

This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the spa…

Cited by 719Source ↗Scholar ↗