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Found 3 papers, 1 papers with code
Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in "digital twin" simulation environments constructed on the fly from small amounts of real-world data.
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
We evaluate this system on a suite of robotic tasks in simulation and demonstrate its effectiveness at learning behaviors both in simulation and the real world.
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.
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