Search Results for author:
Found 16 papers, 2 papers with code
Back to the Manifold: Recovering from Out-of-Distribution States
However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time.
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.
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.
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.
Data-efficient visuomotor policy training using reinforcement learning and generative models
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
Human-centered collaborative robots with deep reinforcement learning
We present a reinforcement learning based framework for human-centered collaborative systems.
Imitating by generating: deep generative models for imitation of interactive tasks
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals.
Skew-Explore: Learn faster in continuous spaces with sparse rewards
In this work, we introduce an exploration approach based on maximizing the entropy of the visited states while learning a goal-conditioned policy.
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs.
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.
Meta-Learning for Multi-objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives.
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.
