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Found 6 papers, 2 papers with code
BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation
To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interacting with a wide and unseen range of everyday objects.
Natural Language Can Help Bridge the Sim2Real Gap
We demonstrate that training the image encoder to predict the language description or the distance between descriptions of a sim or real image serves as a useful, data-efficient pretraining step that helps learn a domain-invariant image representation.
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.
