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Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
TLDR
Simulated experiments show that the proposed approach to automatically and efficiently co-adapt a robot morphology and its controller requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co- Adaptation of robot designs in the real world.
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Sparse Latent Space Policy Search
TLDR
A reinforcement learning method for sample-efficient policy search that exploits correlations between control variables, particularly frequent in motor skill learning tasks, and outperforms state-of-the-art policy search methods.
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Residual Learning From Demonstration: Adapting DMPs for Contact-Rich Manipulation
TLDR
This work proposes “residual Learning from Demonstration” (rLfD), a framework that combines DMPs with Reinforcement Learning (RL) to learn a residual correction policy and suggests that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of D MPs.
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Latent space policy search for robotics
TLDR
This paper presents a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies by performing dimensionality reduction as a preprocessing step but naturally combines it with policy search.
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Extracting bimanual synergies with reinforcement learning
  • K. Luck, H. B. Amor
  • Computer Science
    IEEE/RSJ International Conference on Intelligent…
  • 1 September 2017
TLDR
It is discussed how synergies can be learned through latent space policy search and an extension of the algorithm for the re-use of previously learned synergies for exploration is introduced and introduced.
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From the Lab to the Desert: Fast Prototyping and Learning of Robot Locomotion
TLDR
The findings of this study show that static policies developed in the laboratory do not translate to effective locomotion strategies in natural environments, and sample-efficient reinforcement learning can help to rapidly accommodate changes in the environment or the robot.
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Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient
TLDR
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, leading to a symbiotic relationship between the deep reinforcement learning algorithm and the latent trajectory optimizer.
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Residual Learning from Demonstration: Adapting Dynamic Movement Primitives for Contact-rich Insertion Tasks
TLDR
This work proposes a framework called residual learning from demonstration (rLfD) that combines dynamic movement primitives (DMP) that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy and shows that rLFD outperforms alternatives and improves the generalisation abilities of DMPs.
Residual Learning from Demonstration
TLDR
This work proposes residual learning from demonstration (rLfD), a framework that combines dynamic movement primitives that rely on behavioural cloning with a reinforcement learning (RL) based residual correction policy that outperforms alternatives and improves the generalisation abilities of DMPs.
Bio-inspired Robot Design Considering Load-Bearing and Kinematic Ontogeny of Chelonioidea Sea Turtles
TLDR
The physical implications of variation in fin shape and orientation that correspond to ontogenetic changes observed in sea turtles are explored, and mimicry of these variations in a robotic system confer greater load-bearing capacity and energy efficiency, at the expense of velocity.
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