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Found 4 papers, 1 papers with code
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks
To our knowledge, this is the first work to show that simultaneously conditioning a multi-task robotic manipulation policy on both demonstration and language embeddings improves sample efficiency and generalization over conditioning on either modality alone.
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems.
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task.
