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Found 15 papers, 3 papers with code
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Data-efficient Hindsight Off-policy Option Learning
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm.
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data.
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.
Compositional Transfer in Hierarchical Reinforcement Learning
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
General Reinforcement Learning
Hierarchical Reinforcement Learning
+2
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
We present a method for fast training of vision based control policies on real robots.
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL).
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards.
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics.
