no code implementations • 21 Oct 2022 • Chia-Man Hung, Shaohong Zhong, Walter Goodwin, Oiwi Parker Jones, Martin Engelcke, Ioannis Havoutis, Ingmar Posner
We present a novel approach 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.
no code implementations • 2 May 2022 • Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.
no code implementations • 9 Dec 2021 • Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
no code implementations • 5 Jul 2021 • Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Michael A. Osborne, Ingmar Posner
We provide a theoretical analysis of Deep Sets which shows that this universal approximation property is only guaranteed if the model's latent space is sufficiently high-dimensional.
1 code implementation • NeurIPS 2021 • Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations.
1 code implementation • 13 Jul 2020 • Martin Engelcke, Oiwi Parker Jones, Ingmar Posner
A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision.
no code implementations • 3 Jul 2020 • Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon, Ioannis Havoutis, Ingmar Posner
In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach.
1 code implementation • NeurIPS 2020 • Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, Andrea Vedaldi
We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects.
1 code implementation • ICLR 2020 • Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, Ingmar Posner
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Ranked #1 on Image Generation on Multi-dSprites
no code implementations • 25 Jan 2019 • Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne
Recent work on the representation of functions on sets has considered the use of summation in a latent space to enforce permutation invariance.
5 code implementations • CVPR 2018 • Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Submanifold sparse convolutional networks
Ranked #4 on 3D Semantic Segmentation on SensatUrban
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
no code implementations • 21 Sep 2016 • Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner
This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs).
Ranked #1 on Object Detection on KITTI Pedestrians Easy