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Questions and Answers
Which of the following methods were traditionally used for natural language tasks?
Which of the following methods were traditionally used for natural language tasks?
Large language models are trained on relatively small datasets.
Large language models are trained on relatively small datasets.
False (B)
What is the name of the language model with 175 billion parameters?
What is the name of the language model with 175 billion parameters?
GPT-3
The process of breaking down text into meaningful units is called ______.
The process of breaking down text into meaningful units is called ______.
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Match the following terms with their definitions:
Match the following terms with their definitions:
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What is a common example of a stop word?
What is a common example of a stop word?
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Which of the following are examples of preprocessing steps for text?
Which of the following are examples of preprocessing steps for text?
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Tokenization is a process that only applies to large language models.
Tokenization is a process that only applies to large language models.
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Tokenisation is a process of converting text into a set of individual characters.
Tokenisation is a process of converting text into a set of individual characters.
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What is a common tokenisation strategy mentioned in the text?
What is a common tokenisation strategy mentioned in the text?
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Words like "a", "the", and "etc." are often considered ______ in language understanding tasks.
Words like "a", "the", and "etc." are often considered ______ in language understanding tasks.
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What is the primary purpose of preprocessing steps in language processing?
What is the primary purpose of preprocessing steps in language processing?
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Octo, similar to OpenVLA, is built on a foundation of closed-source components.
Octo, similar to OpenVLA, is built on a foundation of closed-source components.
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What are the two ways a goal can be specified for Octo's goal-conditioned policy?
What are the two ways a goal can be specified for Octo's goal-conditioned policy?
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Foundation models are trained on specific datasets for particular tasks.
Foundation models are trained on specific datasets for particular tasks.
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The GPT family of models are an example of a ______ model.
The GPT family of models are an example of a ______ model.
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What is the defining characteristic of a foundation model in terms of its training data?
What is the defining characteristic of a foundation model in terms of its training data?
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What type of learning is used to train foundation models?
What type of learning is used to train foundation models?
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Match the following terms related to vision transformers with their descriptions:
Match the following terms related to vision transformers with their descriptions:
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Vision transformers were initially developed specifically for image recognition tasks.
Vision transformers were initially developed specifically for image recognition tasks.
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How does a vision transformer process an image for recognition?
How does a vision transformer process an image for recognition?
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Which of the following is NOT a characteristic of vision transformers?
Which of the following is NOT a characteristic of vision transformers?
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A diffusion policy generates actions through a probabilistic ______ process.
A diffusion policy generates actions through a probabilistic ______ process.
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Which of the following is NOT a parameter in the denoising process of a diffusion policy?
Which of the following is NOT a parameter in the denoising process of a diffusion policy?
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The denoising network ϵθ in a diffusion policy learns to approximate noise added to ground-truth actions.
The denoising network ϵθ in a diffusion policy learns to approximate noise added to ground-truth actions.
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What is the purpose of the loss function L in the training of a diffusion policy?
What is the purpose of the loss function L in the training of a diffusion policy?
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Match the following terms to their appropriate definitions:
Match the following terms to their appropriate definitions:
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What type of architecture does Octo use for processing inputs?
What type of architecture does Octo use for processing inputs?
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Octo is limited to using only one camera view for its operations.
Octo is limited to using only one camera view for its operations.
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What is the primary method used by Octo to generate actions?
What is the primary method used by Octo to generate actions?
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A goal-conditioned policy in Octo can specify goals using either an image or ______.
A goal-conditioned policy in Octo can specify goals using either an image or ______.
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Match the following parameters used in the denoising process of a diffusion policy:
Match the following parameters used in the denoising process of a diffusion policy:
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Which of the following features distinguishes Octo from OpenVLA?
Which of the following features distinguishes Octo from OpenVLA?
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The denoising network in the diffusion policy learns to inject noise to the actions.
The denoising network in the diffusion policy learns to inject noise to the actions.
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What is used to create readout tokens in Octo?
What is used to create readout tokens in Octo?
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What is the current trend in training policies within robotics?
What is the current trend in training policies within robotics?
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Open X-Embodiment is rarely used for model training.
Open X-Embodiment is rarely used for model training.
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What architecture forms the basis of large language models?
What architecture forms the basis of large language models?
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The conditions under which generalisation between environment conditions and robots is possible are _____ defined.
The conditions under which generalisation between environment conditions and robots is possible are _____ defined.
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Match the following challenges in robot foundation models to their descriptions:
Match the following challenges in robot foundation models to their descriptions:
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Which method is commonly associated with multimodal learning?
Which method is commonly associated with multimodal learning?
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Small-scale training and fine-tuning are easily achievable for robot foundation models.
Small-scale training and fine-tuning are easily achievable for robot foundation models.
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What is one application of robot foundation models?
What is one application of robot foundation models?
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Vision-language models are trained on aligned _____ and language datasets.
Vision-language models are trained on aligned _____ and language datasets.
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What is a key feature of contemporary vision-language models?
What is a key feature of contemporary vision-language models?
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Robot foundation models are optimized for low computational requirements.
Robot foundation models are optimized for low computational requirements.
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What is the primary purpose of attention layers in transformer architecture?
What is the primary purpose of attention layers in transformer architecture?
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Current robot foundation models have no _____ guarantees.
Current robot foundation models have no _____ guarantees.
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Which of the following is a limitation mentioned for robot foundation models?
Which of the following is a limitation mentioned for robot foundation models?
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Study Notes
Language-Based Learning: A Short Overview of Contemporary Language Use in Robotics
- This presentation covers language-based learning, specifically its use in robotics.
- The speaker, Dr. Alex Mitrevski, delivered this presentation in the winter semester of 2024/25.
Structure
- (Large) Language models
- Robot learning and language
(Large) Language Models
- Language models are computational models for language processing, understanding, and generation.
- Natural language tasks were previously performed using classical machine learning, such as Naive Bayes for text classification.
- Large language models (LLMs) are neural networks trained on massive datasets, featuring a large number of parameters. Example: GPT-3 has 175 billion parameters.
- LLMs are computational models that enable language processing, understanding, and sometimes generation.
Language Models
- Language models enable language processing, understanding, and sometimes generation.
- Previously, natural language tasks like text classification relied on classical machine learning.
Tokenization
- When processing text, a variety of preprocessing steps are applied before language processing.
- This includes removing stop words (e.g., "a," "the") and punctuation, as they are often irrelevant.
- Tokenization is the process of converting text into constituent entities (typically words), a fundamental step in pre-processing.
- Common tokenization strategies involve converting text into individual words, with subsequent processing performed at the word level.
- Hierarchical token representations are also possible, initiating tokenisation at the character or sub-word level.
Word Embeddings
- Numerical representations of tokens are necessary for computations using models like neural networks.
- Bag-of-words representation and term frequency-inverse document frequency (TF-IDF) are examples of classical methods.
- Word embeddings numerically represent tokens, typically using a fixed-size vector space (k) learned by a neural network.
- Embeddings in models are frequently represented as one-hot encoded vectors.
- Examples of common word embeddings include word2vec, BERT, and ELMo.
- Word embeddings encode tokens and put similar meanings in close proximity.
Transformer Architecture
- Most LLMs use transformer architecture.
- The core component is the attention layer, which calculates token importance factors by considering the context of surrounding tokens.
- Multi-head attention layers combine multiple attention layers' outputs.
Why Does Language Matter for Robotics?
- The use of language improves the ability of human-robot communication.
- Language reduces the reliance on specialized, less intuitive, and natural communication interfaces.
- Language improves task description, enabling simplified explanations.
- Language acts as a data source (written format), providing relevant data for human-centered environments.
Foundation Models
- Foundation models are large neural network-based models trained on diverse data.
- They can be trained on a single data modality or multimodal data (e.g., text, audio, images).
- Foundation models are pretrained and can be further refined for specific tasks (transfer learning).
- GPT family models are examples of foundation models.
Vision Transformers
- Transformers were initially used for language processing and have recently been adapted for images.
- A vision transformer splits an image into patches, creates an embedding for each, and processes them sequentially.
- The patches are considered as image tokens to allow processing through the transformer architecture.
- The attention layers operate independently of the modality type if appropriately embedded input modality.
Vision-Language Models (VLMs)
- VLMs are models that combine visual and language inputs for making predictions.
- They ground language to real-world concepts and entities.
- VLMs are trained using contrastive learning techniques.
- VLMs align visual and language data.
Contrastive Learning
- Contrastive learning focuses on learning distance functions between similar and dissimilar inputs, encouraging similar inputs to be closer in the embedding space.
- The method works in both single-modality and multimodal embeddings.
- The focus is on producing a better representation of similar inputs.
Vision-Language-Action Models (VLAs)
- VLMs are not trained for or designed for robot control, instead, they are used for visual question-answer tasks.
- VLAs represent robot actions as discrete tokens, with predictions being de-tokenised to align with the action.
- Action prediction relies on end-effector delta actions and gripper positioning.
RT-X: Robot-Agnostic Foundation Models
- RT-X is a collection of foundation models for robotics trained on the Open X-Embodiment dataset.
- Each model architecture has two variations.
- The focus of RT-X is on generating robot actions for open-source research and adaptability to multiple robots and environments.
OpenVLA
- OpenVLA is a vision-language-action model pretrained on a subset of the Open X-Embodiment dataset.
- Includes additional training datasets.
- Uses a predefined visual input view (third-person).
- Architecturally based on a pretrained VLM.
Octo
- Octo is a transformer-based model trained on a subset of Open X-Embodiment.
- It's a goal-conditioned policy applicable to images or text.
- Uses a diffusion model for generating robot actions.
Diffusion Policy
- Octo applies diffusion policy, a visuomotor policy that generates actions through a probabilistic diffusion process.
- The denoising process (governed by learned network) creates a gradient-based solution.
Summary of Observations
- Vision-language models are actively developed, with recurring new models.
- Open X-Embodiment is a central dataset for training
- Models are often generalized and typically trained using multiple GPUs over multiple days.
- Further research is needed on safety and generalisation to maintain efficiency, and to improve robot reliability and performance.
Next Lecture: Explainable Robotics
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Description
This quiz explores the role of language-based learning in robotics, focusing on large language models and their applications. Discover how these advanced models enhance robot learning and language understanding, as discussed by Dr. Alex Mitrevski in his recent presentation. Test your knowledge on contemporary language use in robotics and its significance.
