Search Results for author: Kevin Sebastian Luck

Found 6 papers, 1 papers with code

Co-Imitation: Learning Design and Behaviour by Imitation

no code implementations2 Sep 2022 Chang Rajani, Karol Arndt, David Blanco-Mulero, Kevin Sebastian Luck, Ville Kyrki

To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state distributions of the demonstrator.

Imitation Learning

Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning

1 code implementation12 Jul 2022 Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna, Stefano V. Albrecht

Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training.

Disentanglement reinforcement-learning +1

What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks

no code implementations3 Nov 2021 Kevin Sebastian Luck, Roberto Calandra, Michael Mistry

The co-adaptation of robot morphology and behaviour becomes increasingly important with the advent of fast 3D-manufacturing methods and efficient deep reinforcement learning algorithms.

reinforcement-learning Reinforcement Learning (RL)

Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation

no code implementations18 Aug 2020 Todor Davchev, Kevin Sebastian Luck, Michael Burke, Franziska Meier, Stefan Schaal, Subramanian Ramamoorthy

Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC) but can struggle in the context of insertion.

Behavioural cloning Friction +1

Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

no code implementations15 Nov 2019 Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra

Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies.

reinforcement-learning Reinforcement Learning (RL)

Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient

no code implementations15 Nov 2019 Kevin Sebastian Luck, Mel Vecerik, Simon Stepputtis, Heni Ben Amor, Jonathan Scholz

This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding.

Continuous Control reinforcement-learning +1

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