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Found 9 papers, 5 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.
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
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.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents
By training 240 representations and over 10, 000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents.
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning
Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence.
Out-of-Distribution Generalization
reinforcement-learning
+3
TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade.
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation.
Robotics
