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Found 5 papers, 1 papers with code
ASID: Active Exploration for System Identification in Robotic Manipulation
In this work, we propose a learning system that can leverage a small amount of real-world data to autonomously refine a simulation model and then plan an accurate control strategy that can be deployed in the real world.
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies.
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields.
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space
Our experimental results show that PTP can generate feasible sequences of subgoals that enable the policy to efficiently solve the target tasks.
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks.
