From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
(2405.04798)Abstract
Hierarchical control for robotics has long been plagued by the need to have a well defined interface layer to communicate between high-level task planners and low-level policies. With the advent of LLMs, language has been emerging as a prospective interface layer. However, this has several limitations. Not all tasks can be decomposed into steps that are easily expressible in natural language (e.g. performing a dance routine). Further, it makes end-to-end finetuning on embodied data challenging due to domain shift and catastrophic forgetting. We introduce our method -- Learnable Latent Codes as Bridges (LCB) -- as an alternate architecture to overcome these limitations. \method~uses a learnable latent code to act as a bridge between LLMs and low-level policies. This enables LLMs to flexibly communicate goals in the task plan without being entirely constrained by language limitations. Additionally, it enables end-to-end finetuning without destroying the embedding space of word tokens learned during pre-training. Through experiments on Language Table and Calvin, two common language based benchmarks for embodied agents, we find that \method~outperforms baselines (including those w/ GPT-4V) that leverage pure language as the interface layer on tasks that require reasoning and multi-step behaviors.
Overview
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The paper introduces a new method called Latent Codes as Bridges (LCB) that connects LLMs with low-level robot control policies to enhance robot autonomy and adaptability without compromising the distinct learning abilities of each layer.
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LCB features a flexible goal communication system and maintains efficiency by updating action decisions based on sensory feedback, while preserving the language understanding and reasoning capabilities of LLMs.
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The LCB method has demonstrated superior performance in complex task environments compared to traditional models and shows significant promise for advancing autonomous robotic systems.
Exploring Latent Code as Bridges (LCB) for Integrating LLMs and Low-Level Policies in Robotics
Context and Challenge
In the quest to equip robots with higher autonomy and adaptability, combining high-level reasoning provided by LLMs and precise low-level action policies has been a significant area of focus. The typical hurdle here lies in seamlessly connecting the two layers: the abstract, language-based decision-making level and the concrete, action-executing level. Traditional methods either resort to rigidly predefined skills that limit flexibility or use natural language as a direct interface, which can distort the model's reasoning capabilities through catastrophic forgetting during fine-tuning.
Innovative Approach: Latent Codes as Bridges (LCB)
Enter the method of Latent Codes as Bridges (LCB), which intrepidly addresses these constraints by introducing a learnable latent code acting as an intermediary or 'bridge' between the LLMs and low-level policies. This approach maintains the integrity and independent learning capacities of both layers. Here’s how it stands out:
- Flexible Goal Communication: By not binding the communication strictly to natural language, LCB allows for a more flexible transmission of goals to the low-level policy, which is particularly beneficial for complex tasks not easily describable in language.
- Preservation of Model Capabilities: The LCB approach does not entail extensive overhauling of the LLM during policy learning, hence preserving its language understanding and reasoning faculties.
- Efficiency in Execution: At runtime, the system can update action decisions frequently based on sensory feedback, while higher-level reasoning updates occur less frequently, thus optimizing computational resources.
Practical Implementation
How is LCB applied practically? Here’s the rundown:
- Architecture Setup: The system integrates a text and vision-inclusive LLM with a low-level policy model. Key to this architecture is the addition of a latent token (
<action>) within the LLM’s scope, trained to encapsulate action directives internally. - Training and Data Handling: To train such a model, both text and vision data are treated to align closely with real-world interaction patterns—this includes formatting instructional data in conversational templates and focusing training on high-level plan derivation followed by specific action sequences.
- Hierarchical Control Strategy: During operation, the high-level LLM offers plans or objectives which are then funneled through the latent code into the low-level policy for action execution, bridging the conceptual gap effectively between planning and acting.
Comparative Analysis
To underscore its efficacy, the LCB method was tested against conventional baselines using established benchmarks like Language Table and Calvin, where it showed considerable improvements:
- Enhanced Performance: For tasks requiring intricate reasoning or multi-step execution, LCB significantly outperformed models using just direct language interfacing.
- Robustness in Varied Scenarios: Whether in simplified table-top environments or more complex settings involving physical robot interactions, LCB demonstrated superior task handling and adaptability.
Future Implications and Developments
The innovative integration strategy LCB proposes is poised to redefine how robotic systems assimilate high-level planning with operational agility. This could lead to broader applications in more dynamic environments, enhancing the robot’s real-world effectiveness. Moreover, the approach opens fertile ground for further research into more nuanced models of interaction between machine learning layers, potentially leading to smarter, more intuitive autonomous systems.
Conclusion
LCB stands as a promising advancement in robotic control systems, harmonizing high-level cognitive functions with the practical necessities of dynamic action execution. By bridging the gap with a latent code, it respects the intrinsic capabilities of both reasoning and action-oriented models, paving the way for more sophisticated, responsive robotic behaviors. The ongoing exploration and optimization of such systems will no doubt continue to push the boundaries of what autonomous systems can achieve.
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