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Found 7 papers, 4 papers with code
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory
To address this, we propose a new approach to apply verification methods from control theory to learned value functions.
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies
However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory.
Improving Behavioural Cloning with Positive Unlabeled Learning
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems.
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.
Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts.
Solving the Real Robot Challenge using Deep Reinforcement Learning
This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a challenge in which a three-fingered robot must carry a cube along specified goal trajectories.
