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Real-world robot applications of foundation models: A review
Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …
Language Models (VLMs), trained on extensive data, facilitate flexible application across …
Voxposer: Composable 3d value maps for robotic manipulation with language models
Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …
Octo: An open-source generalist robot policy
Large policies pretrained on diverse robot datasets have the potential to transform robotic
learning: instead of training new policies from scratch, such generalist robot policies may be …
learning: instead of training new policies from scratch, such generalist robot policies may be …
Foundation models in robotics: Applications, challenges, and the future
We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …
learning models in robotics are trained on small datasets tailored for specific tasks, which …
A systematic survey of prompt engineering on vision-language foundation models
Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …
Zero-shot robotic manipulation with pretrained image-editing diffusion models
If generalist robots are to operate in truly unstructured environments, they need to be able to
recognize and reason about novel objects and scenarios. Such objects and scenarios might …
recognize and reason about novel objects and scenarios. Such objects and scenarios might …
Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …
Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning
Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …
A comprehensive survey of data augmentation in visual reinforcement learning
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …
visual inputs, has demonstrated significant potential in various domains. However …
Generative skill chaining: Long-horizon skill planning with diffusion models
Long-horizon tasks, usually characterized by complex subtask dependencies, present a
significant challenge in manipulation planning. Skill chaining is a practical approach to …
significant challenge in manipulation planning. Skill chaining is a practical approach to …