18 citations · 32 across the 7 of their papers we have counts for
7 papers
Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models
Nicolò Botteghi, Federico Califano, Mannes Poel +1
We present a framework for safety-critical optimal control of physical systems based on denoising diffusion probabilistic models (DDPMs). The technology of control barrier function…
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
Nicolò Botteghi, Mannes Poel, Christoph Brune
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguo…
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Nicolò Botteghi, Luuk Grefte, Mannes Poel +4
Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, t…
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi, Khaled Alaa, Mannes Poel +4
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, su…
Low Dimensional State Representation Learning with Reward-shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs +5
Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
Nicolò Botteghi, Beril Sirmacek, Khaled A. A. Mustafa +2
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw…