Quantized Skill Transformer: Self-Supervised Learning of Transferable Action Abstractions for Continuous Control

Core Concepts

Quantized Skill Transformer (QueST) learns a flexible and structured discrete latent space of action abstractions (skills) that can be effectively leveraged for multitask and few-shot imitation learning in continuous control tasks.

Abstract

The paper presents Quantized Skill Transformer (QueST), a novel architecture for learning transferable action abstractions (skills) in a discrete latent space. The key ideas are:

Encoder-Decoder Architecture:

The encoder ϕθ maps action sequences to a sequence of discrete latent codes (skill tokens) using causal convolutions and masked self-attention.
The decoder ψθ reconstructs the original action sequence by cross-attending to the skill tokens, enabling flexible modeling of variable-length action sequences.
The causal structure of the encoder-decoder encourages the model to learn semantically meaningful and transferable skill representations.

Skill Prior:

After training the encoder-decoder, a skill prior πφ is trained to autoregressively predict the sequence of skill tokens conditioned on task embeddings and observation history.
The skill prior uses a transformer decoder to effectively model the dependencies between skill tokens, enabling compositional reasoning within the skill space.

The authors evaluate QueST on challenging multitask and few-shot imitation learning benchmarks, including LIBERO-90, LIBERO-LONG, and MetaWorld ML45. QueST outperforms state-of-the-art baselines by a significant margin, demonstrating the effectiveness of its learned skill representations for transfer to new tasks. The authors also conduct extensive ablations to validate the key design choices behind QueST.

Stats

QueST achieves an 88.6% mean success rate on the LIBERO-90 multitask benchmark, outperforming the next best baseline by 8%.
On the LIBERO-LONG few-shot benchmark, QueST achieves a 68.8% mean success rate, a 14% improvement over the next best baseline.
On the MetaWorld ML45 benchmark, QueST achieves a 91.7% mean success rate in the multitask setting and a 71.9% mean success rate in the few-shot setting.

Quotes

"Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set out to tackle this problem, unsolved it remains."
"We hypothesize that learning temporal action abstractions using latent variable models (LVMs), which learn to map data to a compressed latent space and back, is a promising direction towards low-level skills that can readily be used for new tasks."

Key Insights Distilled From

QueST: Self-Supervised Skill Abstractions for Learning Continuous Control

by Atharva Mete... at arxiv.org 09-12-2024

https://arxiv.org/pdf/2407.15840.pdf

Deeper Inquiries

How can the learned skill representations in QueST be further leveraged for hierarchical task planning and execution?

The learned skill representations in the Quantized Skill Transformer (QueST) can be effectively utilized for hierarchical task planning and execution by integrating them into a multi-level decision-making framework. Hierarchical task planning involves decomposing complex tasks into simpler, manageable subtasks, which can be represented as sequences of skills learned by QueST.

Skill Decomposition: The discrete latent space of QueST allows for the representation of low-level skills as atomic actions or motion primitives. These skills can be organized hierarchically, where higher-level tasks are composed of sequences of lower-level skills. For instance, a task like "making a sandwich" can be broken down into subtasks such as "getting the bread," "spreading butter," and "placing the ingredients."

Skill Composition: By leveraging the autoregressive nature of QueST, planners can generate sequences of skills that are contextually relevant to the current state of the environment. This allows for dynamic adaptation to changes in the environment or task requirements, enhancing the robot's ability to execute complex tasks in real-time.

Task Generalization: The shared representations learned by QueST can facilitate the transfer of skills across different tasks. This means that once a robot has learned a set of skills, it can apply them to new, unseen tasks by reusing and recombining these skills, thus improving efficiency and reducing the need for extensive retraining.

Integration with Higher-Level Planning: QueST's skill representations can be integrated with higher-level planning algorithms, such as those based on Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). This integration allows for the formulation of policies that not only consider immediate actions but also long-term goals, leading to more effective task execution.

What are the limitations of the current QueST architecture, and how can it be extended to handle more diverse and open-ended task distributions?

While the QueST architecture demonstrates significant advancements in learning sharable skills, it does have limitations that can be addressed to enhance its applicability to more diverse and open-ended task distributions.

Task Similarity: The current architecture primarily excels in environments where tasks are structurally similar to those seen during training. This reliance on task similarity may hinder performance in scenarios with highly diverse or novel tasks. To address this, QueST could be extended by incorporating meta-learning techniques that enable the model to adapt quickly to new tasks with minimal data.

Scalability of the Codebook: The fixed size of the discrete latent space may limit the model's ability to capture a wide range of motion primitives, especially in complex environments. Expanding the codebook size or employing a dynamic codebook that can grow based on the diversity of tasks encountered could enhance the model's representational capacity.

Incorporation of Contextual Information: The current QueST architecture does not fully leverage contextual information beyond the immediate observations and task descriptions. Integrating additional contextual cues, such as environmental states or user preferences, could improve the model's ability to generalize across tasks and adapt to varying conditions.

Exploration of Inductive Biases: The architecture currently focuses on causality as an inductive bias. Future iterations could explore other biases, such as geometric invariance or temporal consistency, to enhance the model's robustness in learning and executing tasks in dynamic environments.

Open-Ended Learning: To handle open-ended task distributions, QueST could be augmented with reinforcement learning techniques that allow the model to continuously learn from interactions with the environment. This would enable the robot to refine its skills and adapt to new tasks as they arise, fostering a more flexible and autonomous learning process.

Can the principles behind QueST's structured discrete latent space be applied to other domains beyond robotics, such as language or video generation?

Yes, the principles behind QueST's structured discrete latent space can be effectively applied to other domains beyond robotics, including language processing and video generation.

Language Processing: In natural language processing (NLP), the concept of discrete latent spaces can be utilized to represent various linguistic constructs, such as phrases or sentences, as discrete tokens. This approach can enhance tasks like text generation, translation, and summarization by allowing models to learn and manipulate structured representations of language. For instance, a model could learn to generate coherent paragraphs by composing sequences of learned sentence structures, similar to how QueST composes action sequences.

Video Generation: In the realm of video generation, structured discrete latent spaces can be employed to represent frames or sequences of frames as discrete codes. This allows for the modeling of complex temporal dynamics in video data. By learning to generate video sequences from these discrete representations, models can produce coherent and contextually relevant video content. The autoregressive nature of QueST can be adapted to predict future frames based on past frames, facilitating the generation of realistic video sequences.

Generative Models: The principles of quantization and structured representation can also be applied to generative models in various domains, such as music or art. By representing musical notes or artistic strokes as discrete tokens, models can learn to compose new pieces by sampling from the learned latent space, similar to how QueST generates action sequences.

Cross-Modal Applications: The structured discrete latent space can facilitate cross-modal applications, where skills or actions in one domain can inform or enhance learning in another. For example, skills learned in a robotic manipulation task could inform language generation models about the actions being described, leading to more contextually aware and relevant outputs.

In summary, the principles underlying QueST's architecture can be generalized to various domains, promoting the development of models that leverage structured representations for improved performance in complex tasks across different fields.