Source-linked AI summary
Robot Behavior-Tree-Based Task Generation with Large Language Models
Yue Cao, C. S. George Lee
TL;DR
Existing behavior-tree task generators rely on predefined primitive libraries, limiting generation to their given domains. The paper combines LLMs, Phase-Step prompting, and retrieval of similar behavior trees to generate tasks across domains; its ablation reports that Phase-Step prompts preserve tree structure better than prompts without them.
Problem
Existing behavior-tree generation approaches rely on predefined primitive-task libraries and cannot generate tasks outside their given domains.
Source: §1 · p. 2Method
The approach uses Phase-Step prompting to generate hierarchical behavior-tree tasks, constructs complete trees, and retrieves a similar source tree to condition the prompt.
Source: §1 · p. 3Results
Without Phase-Step prompting, generated tasks had structure ratios close to zero; with it, ratios were close to or above the source tasks’ 2/3 ratio.
Source: §4.3 · p. 12Takeaways & Limitations
The paper concludes that cross-domain task transfer reduces end-users’ workload in designing new robot tasks.
Source: §5 · p. 14ChatGPT frequently failed on rare tasks, including the “Vince Lombardi Trophy crafting” task.
Source: §4.5 · p. 13
Abstract
from arXiv · showhide
Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is time-consuming for robot end-users, thus there is a need for investigating automatic behavior-tree-based task generation. Prior behavior-tree-based task generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree-based task generation approach that utilizes state-of-the-art large language models. We propose a Phase-Step prompt design that enables a hierarchical-structured robot task generation and further integrate it with behavior-tree-embedding-based search to set up the appropriate prompt. In this way, we enable an automatic and cross-domain behavior-tree task generation. Our behavior-tree-based task generation approach does not require a set of pre-defined primitive tasks. End-users only need to describe an abstract desired task and our proposed approach can swiftly generate the corresponding behavior tree. A full-process case study is provided to demonstrate our proposed approach. An ablation study is conducted to evaluate the effectiveness of our Phase-Step prompts. Assessment on Phase-Step prompts and the limitation of large language models are presented and discussed.
1. Introduction
Behavior trees offer modular, reusable, readable robot-task representations, but existing generation approaches rely on fixed primitive-task libraries and remain limited to their domains. This paper uses large language models, Phase-Step prompting, and behavior-tree task retrieval to generate hierarchical tasks across domains.
1. Introduction
- Behavior trees coordinate robot primitive tasks with control-flow nodes and are described as more modular, flexible, and readable than several traditional task representations.The cited alternatives are state machines, subsumption architectures, and decision trees.Source: §1 · p. 1
- Existing behavior-tree generation approaches depend on predefined primitive-task libraries and cannot generate tasks outside their given domains.The paper gives car assembly as an example: absent primitives such as aligning a wheel or welding a door prevent generation.Source: §1 · p. 2
- Prior LLM-based robot-task generation is limited to sequential tasks, while behavior trees offer more reusable and readable modular representations.The paper positions behavior-tree generation as the next step beyond sequential-task generation.Source: §1 · p. 3
- The approach combines a Phase-Step prompt, behavior-tree construction, and automatic selection of a similar source task from a task knowledge base.The selected source tree and target description set up the prompt; the approach aims to generate a tree for a task in a new domain.Source: §1 · p. 3
- Phase-Step prompting extends LLM-based generation from sequential to hierarchical tasks, while behavior-tree modularity and reusability ease end-user design.The paper presents LLM-based behavior-tree generation as its first application of state-of-the-art LLMs to behavior trees.Source: §1 · p. 3
2. Background
The background introduces behavior trees as hierarchical task representations and explains their node types and control flow. It then describes large language models and prompt-based learning, where prompt design guides generated output.
2.1. Behavior-Tree Fundamentals
- Behavior trees are rooted task trees whose leaves execute actions or evaluate conditions, while branch nodes specify control flow.Sequence nodes continue until failure; Fallback nodes continue until success.Source: §2.1 · p. 3, Figure 2 · p. 4
- Behavior trees are ticked from the root, and their modular structure supports combining trees and modifying subtrees independently.The paper also notes flexibility and readability as benefits over traditional representations.Source: §2.1 · p. 4
2.2. Large Language Models and Prompt-Based Learning
- Large language models are pretrained neural language models with many parameters, and the paper describes them as generalizable across downstream language tasks.The background frames text generation as a conditional probability distribution over output text given input text.Source: §2.2 · p. 4
- Prompt-based learning converts input into a template with input and output slots, then uses the pretrained model to fill the output slots.Prompt design guides access to existing knowledge and regulates output structure; Table 1 summarizes prompt designs for robot-task generation.Source: §2.2 · p. 4, Table 1 · p. 5
3. Proposed Approach
The approach combines Phase-Step prompting, behavior-tree construction, and automatic source-task selection to generate a complete robot-task behavior tree from a desired task description.
3. Proposed Approach
- The proposed approach has three components: Phase-Step prompting, behavior-tree construction, and automatic source-task selection.The complete pipeline uses a retrieved source tree and target description to prompt the language model to generate a target tree.Source: §3 · p. 5, §3.3 · pp. 7–8
3.1. Phase-Step Prompt
- Phase-Step prompting converts a three-layer tree of Sequence and Action nodes into a structured prompt, then reconstructs the generated Phase-Step output as a tree.The prompt uses source-task procedures and a desired target description; tool use and phase count can optionally be specified.Source: §3.1 · pp. 5–6, Figure 3 · p. 5, Table 2 · p. 6
3.2. Behavior-Tree Construction
- Subtasks are checked against a robot-specific list of capability verbs; those below a similarity threshold are recursively decomposed until primitive.Alternatively, prompt constraints can make subtasks primitive in one expansion, but limit expansion depth to one; extra specifications are represented with Fallback and Condition nodes.Source: §3.2 · pp. 6–7
3.3. Automatic Source-Task Selection
- The target description is matched to behavior trees in a knowledge base, and the highest-similarity tree becomes the source prompt for generation.The pipeline starts with the target description and automates the remaining process.Source: §3.3 · pp. 7–8, Figure 4 · p. 8
4. Case Studies and Evaluations
Across the evaluations, Phase-Step prompts produced more tree-like task structures than a plain prompt, while the prompt’s detail affected generated assembly procedures. The demonstrations also show model differences and limited capability on rare tasks.
4.1. Experimental Setting
- Tests used GPT-3 text-davinci-003 and ChatGPT; GPT-3 was run at temperature 0, while ChatGPT outputs varied and only the first response was recorded.Both encoders used the Universal Sentence Encoder, with a primitive-action similarity threshold of 0.5.Source: §4.1 · p. 8
4.2. Full-Process Demonstration
- Using a wheel-assembly source for desktop assembly, GPT-3 generated four phases and 11 steps, while ChatGPT generated three phases and eight steps.ChatGPT's generated task included a non-primitive “install” action and two irrelevant final steps; in the CPU-install expansion, GPT-3's subtasks were more succinct and interpretable.Source: §4.2 · pp. 9–10, Table 3 · p. 9, Table 4 · p. 10
4.3. Ablation Study
- Without the Phase-Step prompt, average structure ratio R was close to zero, indicating nearly sequential generated tasks; adding it made all R values close to or above 2/3, the source tasks’ original ratio.Source: §4.3 · p. 12
4.4. Phase-Step Prompt Assessment
- For assembly tasks, PS-wheel prompts tended to yield fewer part-mating operations than PS-desktop prompts; the projector example produced one versus five.The authors limited this assessment to assembly because they report no universal metric for generated tasks spanning domains.Source: §4.4 · p. 13, Table 7 · p. 13
4.5. Limitation
- The models showed limited capability on rare tasks: ChatGPT often failed the trophy-crafting task, while GPT-3 generated one but missed Atlas's hydraulic-actuator specialty.ChatGPT did successfully generate an Atlas assembly task that mentioned hydraulic actuators.Source: §4.5 · pp. 13–14
5. Conclusions and Discussions
The paper presents LLM-based behavior-tree task generation as a way to support cross-domain task transfer and reduce end-user design work. Real-world deployment remains constrained by complex factory environments and unresolved safety-regulation integration.
5. Conclusions and Discussions
- The approach uses large language models for robot behavior-tree-based task generation.It is presented as bringing together behavior trees and generative AI in natural language processing.Source: §5 · p. 14
- Cross-domain task transfer reduces end-users’ workload in designing new robot tasks.Source: §5 · p. 14
- Real-world application still requires further study because factory complexity and safety regulations are not yet addressed.The generated manufacturing tasks look reasonable and executable, but do not account for complex factory environments; incorporating industrial safety regulations remains open.Source: §5 · p. 14