27 citations · 46 across the 9 of their papers we have counts for
10 papers
Learning from Demonstration without Demonstrations
Tom Blau, Gilad Francis, Philippe Morere
State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem i…
Structural clustering of volatility regimes for dynamic trading strategies
Arjun Prakash, Nick James, Max Menzies +1
We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We use chan…
Reinforcement Learning with Probabilistically Complete Exploration
Philippe Morere, Gilad Francis, Tom Blau +1
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in th…
OCTNet: Trajectory Generation in New Environments from Past Experiences
Weiming Zhi, Tin Lai, Lionel Ott +2
Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and unders…
Bayesian Local Sampling-based Planning
Tin Lai, Philippe Morere, Fabio Ramos +1
Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic com…
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Tin Lai, Fabio Ramos, Gilad Francis
Sampling efficiency in a highly constrained environment has long been a major challenge for sampling-based planners. In this work, we propose Rapidly-exploring Random disjointed-Tr…
Functional Path Optimisation for Exploration in Continuous Occupancy Maps
Gilad Francis, Lionel Ott, Fabio Ramos
Autonomous exploration is a complex task where the robot moves through an unknown environment with the goal of mapping it. The desired output of such a process is a sequence of pat…
Stochastic Functional Gradient Path Planning in Occupancy Maps
Gilad Francis, Lionel Ott, Fabio Ramos
Planning safe paths is a major building block in robot autonomy. It has been an active field of research for several decades, with a plethora of planning methods. Planners can be g…
Occupancy Map Building through Bayesian Exploration
Gilad Francis, Lionel Ott, Roman Marchant +1
We propose a novel holistic approach for safe autonomous exploration and map building based on constrained Bayesian optimisation. This method finds optimal continuous paths instead…
Stochastic Functional Gradient for Motion Planning in Continuous Occupancy Maps
Gilad Francis, Lionel Ott, Fabio Ramos
Safe path planning is a crucial component in autonomous robotics. The many approaches to find a collision free path can be categorically divided into trajectory optimisers and samp…