Nowadays, robots are increasingly operated in environments shared with humans, where conflicts between human and robot behaviors may compromise safety. This paper presents a proactive behavioral conflict avoidance framework based on the principle of adaptation to trends for quadruped robots that not only ensures the robot's safety but also minimizes interference with human activities. It can proactively avoid potential conflicts with approaching humans or other dynamic objects, whether the robot is stationary or in motion, then swiftly resume its tasks once the conflict subsides. An enhanced approach is proposed to achieve precise human detection and tracking on vibratory robot platform equipped with low-cost hybrid solid-state LiDAR. When potential conflict detected, the robot selects an avoidance point and executes an evasion maneuver before resuming its task. This approach contrasts with conventional methods that remain goal-driven, often resulting in aggressive behaviors, such as forcibly bypassing obstacles and causing conflicts or becoming stuck in deadlock scenarios. The selection of avoidance points is achieved by integrating static and dynamic obstacle to generate a potential field map. The robot then searches for feasible regions within this map and determines the optimal avoidance point using an evaluation function. Experimental results demonstrate that the framework significantly reduces interference with human activities, enhances the safety of both robots and persons.

In this paper, we introduce "InfiAgent-DABench", the first benchmark specifically designed to evaluate LLM-based agents in data analysis tasks. This benchmark contains DAEval, a dataset consisting of 311 data analysis questions derived from 55 CSV files, and an agent framework to evaluate LLMs as data analysis agents. We adopt a format-prompting technique, ensuring questions to be closed-form that can be automatically evaluated. Our extensive benchmarking of 23 state-of-the-art LLMs uncovers the current challenges encountered in data analysis tasks. In addition, we have developed DAAgent, a specialized agent trained on instruction-tuning datasets. Evaluation datasets and toolkits for InfiAgent-DABench are released at https://github.com/InfiAgent/InfiAgent.

Semantic types are a more powerful and detailed way of describing data than atomic types such as strings or integers. They establish connections between columns and concepts from the real world, providing more nuanced and fine-grained information that can be useful for tasks such as automated data cleaning, schema matching, and data discovery. Existing deep learning models trained on large text corpora have been successful at performing single-column semantic type prediction for relational data. However, in this work, we propose an extension of the semantic type prediction problem to JSON data, labeling the types based on JSON Paths. Similar to columns in relational data, JSON Path is a query language that enables the navigation of complex JSON data structures by specifying the location and content of the elements. We use a graph neural network to comprehend the structural information within collections of JSON documents. Our model outperforms a state-of-the-art existing model in several cases. These results demonstrate the ability of our model to understand complex JSON data and its potential usage for JSON-related data processing tasks.