An Autonomous Task Algorithm Based on Behavior Trees for Robot

Behavior trees(BTs) provide a systematic and structured control architecture extensively employed in game AI and robotic behavior control, owing to their modularity, reactivity, and reusability. Nonetheless, manual BTs design requires significant expertise and becomes inefficient as task complexity increases. Recent automation technologies have avoided manual work, but often have high application barriers and face challenges in adapting to new tasks, making it difficult to easily configure them to specific requirements. Code-BT introduces a novel approach that utilizes large language models(LLMs) to automatically generate BTs, representing the task planning process as the process of coding and organizing sequences. By retrieving control flow information from the generated code, BTs can be efficiently constructed to address the complexity and diversity of task planning challenges. Rather than relying on manual design, Code-BT uses task instructions to guide the selection of relevant APIs, and then systematically assembles these APIs into modular code to align with the BTs structure. Finally, action sequences and control logic are extracted from the generated code to construct the BTs. Our approach not only ensures the automation of BTs generation but also guarantees the scalability and adaptability for long-term tasks. Experimental results demonstrate that Code-BT substantially improves LLM performance in BTs generation, achieving improvements ranging from16.67% to 29.17%.

Collaborative construction robots have emerged as a promising alternative to relieve construction workers from both physically and cognitively demanding tasks, contributing to a safer and more productive construction industry. However, communicating with robots is not a trivial task as human workers and robots speak different languages. From the human-centered perspective, allowing human workers to communicate with robots using natural language is desirable because it minimizes additional cognitive load to human workers. Existing studies, however, have been focusing on converting language instructions into sequential actions, leading to a rigid task plan and inability to handle complex situations and unstructured working environments. To address this critical limitation, this paper explores the use of behavior tree (BT), an alternative architecture for describing and controlling complex tasks like excavation. A behavior tree is a hierarchical tree structure that specifies the switching between the agent’s actions (i.e., execution nodes) via control flow nodes. Its modular nature allows the BT of excavation to be generated through linking reusable actions based on the human task descriptions. The resulting BT structure enables the robot to alter its behavior by selecting different tree branches in response to changing working conditions, thus improving its adaptability to dynamic construction environment and its capability of error-handling. In addition, the BT eases the human understanding of robot behavior for debugging and correcting robot behavior. A corresponding framework is proposed for enabling humans to guide a robotic excavator using goal-oriented language instructions. The framework consists of four modules: interpretation and reasoning, knowledge management, structural analysis and parsing, and BT generation. The interpretation and reasoning module decomposes instructions into structured executable intents. The knowledge management module organizes the knowledge for instruction reasoning, including the robot capable skills and its current working environment. Structure analysis and parsing module further grounds the intents and extracts associated parameters, while BT generation module maps the extracted elements with predefined BT nodes, building and refining the BTs of desired tasks. A case illustration is performed to demonstrate the viability of the proposed framework with executable demos. The findings are expected to facilitate efficient and transparent human-robot cooperation in earthmoving construction from a human friendly perspective.

Understanding user intent from her sequential search behaviors, i.e. predicting the intent of each user query in a search session, is crucial for modern Web search engines. However, due to the huge number of user behavior variables and coarse level intent labels defined by human editors, it is very difficult to directly model user behavioral dynamics or user intent dynamics in user search sessions. In this paper, we propose a novel Sparse Hidden-Dynamic Conditional Random Fields (SHDCRF) model for user intent learning from their search sessions. Through incorporating the proposed hidden state variables, SHDCRF aims to learn a substructure, i.e. a set of related hidden variables, for each intent label and they are used to model the intermediate dynamics between user intent labels and user behavioral variables. In addition, SHDCRF learns a sparse relation between the hidden variables and intent labels to make the hidden state variables explainable. Extensive experiment results, on real user search sessions from a popular commercial search engine show that the proposed SHDCRF model significantly outperforms in terms of intent prediction results that those classical solutions such as Support Vector Machine (SVM), Conditional Random Field (CRF) and Latnet-Dynamic Conditional Random Field (LDCRF).

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.

In this paper we present an architecture that embeds an automatic task planner into the Robotic Operating System (ROS). The plans generated by the planner are automatically converted into executable behaviour trees. These behaviour trees are used as the basis of an execution monitoring process that is able to detect when the plan fails, trying to repair it or triggering a replanning process. This makes our architecture able to adapt to unexpected changes in the world, modifying the robot's behaviour in a reactive way while trying to achieve a given goal. We have tested our approach in a simulated scenario.

Conventional search engines usually return a ranked list of web pages in response to a query. Users have to visit several pages to locate the relevant parts. A promising future search scenario should involve: (1) understanding user intents; (2) providing relevant information directly to satisfy searchers' needs, as opposed to relevant pages. In this paper, we present a search paradigm to summarize a query's information from different aspects. Query aspects could be aligned to user intents. The generated summaries for query aspects are expected to be both specific and informative, so that users can easily and quickly find relevant information. Specifically, we use a Composite Query for Summarization method, where a set of component queries are used for providing additional information for the original query. The system leverages the search engine to proactively gather information by submitting multiple component queries according to the original query and its aspects. In this way, we could get more relevant information for each query aspect and roughly classify information. By comparative mining the search results of different component queries, it is able to identify query (dependent) aspect words, which help to generate more specific and informative summaries. The experimental results on two data sets, Wikipedia and TREC ClueWeb2009, are encouraging. Our method outperforms two baseline methods on generating informative summaries.

Investigating senders’ switching intention to smart lockers: An extension of push-pull-mooring model

Robotic systems are increasingly present in dynamic environments. This paper proposes a hierarchical control structure wherein a behavior tree (BT) is used to improve the flexibility and adaptability of an omni-directional mobile robot for point stabilization. Flexibility and adaptability are crucial at each level of the sense–plan–act loop to implement robust and effective robotic solutions in dynamic environments. The proposed BT combines high-level decision making and continuous execution monitoring while applying non-linear model predictive control (NMPC) for the point stabilization of an omni-directional mobile robot. The proposed control architecture can guide the mobile robot to any configuration within the workspace while satisfying state constraints (e.g., obstacle avoidance) and input constraints (e.g., motor limits). The effectiveness of the controller was validated through a set of realistic simulation scenarios and experiments in a real environment, where an industrial omni-directional mobile robot performed a point stabilization task with obstacle avoidance in a workspace.

Compared to the traditional industrial robots that use rigid actuators, the advanced robotic systems are mobile and physically interact with unknown and dynamic environments. Therefore, they need intrinsically safe and compact actuators. In the last two decades, Series Elastic Actuators (SEAs) have been one of the most popular compliant actuators in advanced robotic applications due to their intrinsically safe and compact mechanical structures. The mobility and functionality of the advanced robotic systems are highly related to the torque-density of their actuators. For example, the amount of assistance an exoskeleton robot can provide is determined by the trade-off between the weight and output-torque, i.e., torque-density, of its actuators. As the torque outputs of the actuators are increased, the exoskeleton can expand its capacity yet it generally becomes heavier and bulkier. This has significant impact on the mobility of the advanced robotic systems. Therefore, it is essential to design light-weight actuators which can provide high-output torque. However, this still remains a big challenge in engineering. To this end, this paper proposes a high-torque density SEA for physical robot environment interaction (p-REI) applications. The continuous (peak) output-torque of the proposed compliant actuator is 147Nm (467 Nm) and its weight is less than 2.5kg. It is shown that the weight can be lessened to 1.74, but it comes at cost. The performance of the proposed compliant actuator is experimentally verified.

Web-based image search engines differ from Web search engines greatly. The intents or goals behind human interactions with image search engines are different. In image search, users mainly search images instead of Web pages or online services. It is essential to know why people search for images because user satisfaction may vary as intent varies. Furthermore, image search engines show results differently. For example, grid-based placement is used in image search instead of the linear result list, so that users can browse result list both vertically and horizontally. Different user intents and system UIs lead to different user behavior. Thus, it is hard to apply standard user behavior models developed for general Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the lab-based user study, field study and commercial search log analysis. We then propose user behavior models based on the observation from data analysis to improve the performance of Web image search engines.

With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.

A predictive model for mobility systems capable of understanding the trajectory a user intends to follow in the environment is proposed. Understanding user intention is paramount for any shared-control navigation strategy between a user and an active robotic agent. Equally important however is being able to go beyond simple sample generation to assign probabilistic meaning to the set of possible future trajectories, so most likely scenarios can be assumed. The framework estimates a distribution over possible intentions, proposing a novel generative model predicated on Normalizing Flows which accounts for past behaviours, as traditionally reported in the literature, but also incorporates visual scene information. As the model permits trajectories to be assigned exact likelihoods, tractable density estimates can be readily exploited to finalize an executable intention. Baseline comparisons with the publicly available and widely used KITTI navigational dataset show significant improvements (up to 11.08%) with respect to traditional metrics such as Average and Final Displacement Errors. A novel metric that stands independent of the number of samples is also proposed as a more fitting comparison for future works.

This study investigates the factors influencing user satisfaction (US) and continuous usage intention (UI) of the digital banking platform in Indonesia. Utilizing a quantitative research approach, structural equation modeling (SEM) via SmartPLS was employed to analyze data from 376 users. The study integrates key constructs, including Task-Technology Fit (TTF), System Quality (SQ), Performance Expectancy (PE), US, and UI, into a comprehensive model. The findings confirm that TTF, SQ, PE, and US significantly influence UI. Specifically, higher TTF and SQ directly enhance PE (path coefficient = 0.871, t-value = 92.895) and US (path coefficient = 0.798, t-value = 47.957), positively impacting UI. Performance Expectancy emerged as a stronger predictor of UI (path coefficient = 0.559, t-value = 12.800) compared to the US (path coefficient = 0.245, t-value = 5.229), underscoring the critical role of perceived performance benefits in driving continuous usage. All five hypotheses were supported: TTF positively affects UI (path coefficient = 0.250, t-value = 7.154); SQ positively influences PE and US; PE positively impacts UI; and US positively affects UI. The Sobel test results indicated that PE significantly mediates the relationship between SQ and UI (Z = 12.60), and US also significantly mediates this (Z = 5.19). The R-squared values indicate the explanatory power of the model: PE (0.758), UI (0.956), and US (0.637), demonstrating that the model explains a substantial portion of the variance in these constructs. The study contributes to the literature by validating the integrated model, extending existing models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), and highlighting the importance of technical and perceptual factors in technology adoption. Practically, the results offer actionable insights for digital banking providers. Enhancing TTF and maintaining high SQ are crucial for fostering positive user experiences and encouraging continuous usage. Providers should also emphasize the performance benefits of their platforms to improve PE and UI. Despite its contributions, the study has limitations, including sample size and reliance on self-reported data, which may affect generalizability. Future research could expand the sample size, incorporate objective usage data, and explore additional factors such as social influence and facilitating conditions. Overall, the study provides a robust framework for understanding user behavior in digital banking and offers practical strategies for improving user satisfaction and retention in the industry.

Learning from demonstration is an approach that allows users to personalize a robot's tasks. While demonstrations often focus on conveying the robot's motion or task plans, they can also communicate user intentions through object attributes in manipulation tasks. For instance, users might want to teach a robot to sort fruits and vegetables into separate boxes or to place cups next to plates of matching colors. This paper introduces a novel method that enables robots to learn the semantics of user demonstrations, with a particular emphasis on the relationships between object attributes. In our approach, users demonstrate essential task steps by manually guiding the robot through the necessary sequence of poses. We reduce the amount of data by utilizing only robot poses instead of trajectories, allowing us to focus on the task's goals, specifically the objects related to these goals. At each step, known as a keyframe, we record the end-effector pose, object poses, and object attributes. However, the number of keyframes saved in each demonstration can vary due to the user's decisions. This variability in each demonstration can lead to inconsistencies in the significance of keyframes, complicating keyframe alignment to generalize the robot's motion and the user's intention. Our method addresses this issue by focusing on teaching the higher-level goals of the task using only the required keyframes and relevant objects. It aims to teach the rationale behind object selection for a task and generalize this reasoning to environments with previously unseen objects. We validate our proposed method by conducting three manipulation tasks aiming at different object attribute constraints. In the reproduction phase, we demonstrate that even when the robot encounters previously unseen objects, it can generalize the user's intention and execute the task.

Underlying principles of trajectory formation for human movement in dynamical environments