Search Results for author: Tim Hertweck

Found 8 papers, 0 papers with code

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

no code implementations17 Sep 2021 Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller

Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.

Representation Matters: Improving Perception and Exploration for Robotics

no code implementations3 Nov 2020 Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, tejas kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin Riedmiller

Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.

Simple Sensor Intentions for Exploration

no code implementations15 May 2020 Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess

In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

Disentangled Cumulants Help Successor Representations Transfer to New Tasks

no code implementations25 Nov 2019 Christopher Grimm, Irina Higgins, Andre Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh

This is in contrast to the state-of-the-art reinforcement learning agents, which typically start learning each new task from scratch and struggle with knowledge transfer.

Transfer Learning

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