12 citations · 31 across the 11 of their papers we have counts for
14 papers
Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks
Benjamin David Evans, Raphael Trumpp, Marco Caccamo +4
The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications…
High-performance Racing on Unmapped Tracks using Local Maps
Benjamin David Evans, Hendrik Willem Jordaan, Herman Arnold Engelbrecht
Map-based methods for autonomous racing estimate the vehicle's location, which is used to follow a high-level plan. While map-based optimisation methods demonstrate high-performanc…
High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning
Benjamin David Evans, Herman Arnold Engelbrecht, Hendrik Willem Jordaan
The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can tr…
Credit-cognisant reinforcement learning for multi-agent cooperation
F. Bredell, H. A. Engelbrecht, J. C. Schoeman
Traditional multi-agent reinforcement learning (MARL) algorithms, such as independent Q-learning, struggle when presented with partially observable scenarios, and where agents are…
Bypassing the Simulation-to-reality Gap: Online Reinforcement Learning using a Supervisor
Benjamin David Evans, Johannes Betz, Hongrui Zheng +3
Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the rob…
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
Kevin Eloff, Okko Räsänen, Herman A. Engelbrecht +2
Multi-agent reinforcement learning has been used as an effective means to study emergent communication between agents, yet little focus has been given to continuous acoustic commun…
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language
Kevin Eloff, Herman A. Engelbrecht
Communication between agents in collaborative multi-agent settings is in general implicit or a direct data stream. This paper considers text-based natural language as a novel form…
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Ruan de Kock, Omayma Mahjoub, Sasha Abramowitz +5
Multi-agent reinforcement learning (MARL) research is inherently computationally expensive and it is often difficult to obtain a sufficient number of experiment samples to test hyp…
Reward Signal Design for Autonomous Racing
Benjamin Evans, Herman A. Engelbrecht, Hendrik W. Jordaan
Reinforcement learning (RL) has shown to be a valuable tool in training neural networks for autonomous motion planning. The application of RL to a specific problem is dependent on…
Learning the Subsystem of Local Planning for Autonomous Racing
Benjamin Evans, Hendrik W. Jordaan, Herman A. Engelbrecht
The problem of autonomous racing is to navigate through a race course as quickly as possible while not colliding with any obstacles. We approach the autonomous racing problem with…
Unsupervised feature learning for speech using correspondence and Siamese networks
Petri-Johan Last, Herman A. Engelbrecht, Herman Kamper
In zero-resource settings where transcribed speech audio is unavailable, unsupervised feature learning is essential for downstream speech processing tasks. Here we compare two rece…
Deep motion estimation for parallel inter-frame prediction in video compression
André Nortje, Herman A. Engelbrecht, Herman Kamper
Standard video codecs rely on optical flow to guide inter-frame prediction: pixels from reference frames are moved via motion vectors to predict target video frames. We propose to…
BINet: a binary inpainting network for deep patch-based image compression
André Nortje, Willie Brink, Herman A. Engelbrecht +1
Recent deep learning models outperform standard lossy image compression codecs. However, applying these models on a patch-by-patch basis requires that each image patch be encoded a…
Multimodal One-Shot Learning of Speech and Images
Ryan Eloff, Herman A. Engelbrecht, Herman Kamper
Imagine a robot is shown new concepts visually together with spoken tags, e.g. "milk", "eggs", "butter". After seeing one paired audio-visual example per class, it is shown a new s…