5 citations · 16 across the 5 of their papers we have counts for
8 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…
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…
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…