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Found 10 papers, 4 papers with code
Co-Imitation: Learning Design and Behaviour by Imitation
To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state distributions of the demonstrator.
Online vs. Offline Adaptive Domain Randomization Benchmark
However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
In this work, we introduce a novel learning algorithm called SafeAPT that leverages a diverse repertoire of policies evolved in the simulation and transfers the most promising safe policy to the real robot through episodic interaction.
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult.
Affine Transport for Sim-to-Real Domain Adaptation
In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning.
Few-shot model-based adaptation in noisy conditions
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection.
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware.
Affordance Learning for End-to-End Visuomotor Robot Control
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide.
