Vincenzo Suriani

Task planning for robots in real-life settings is inherently complex due to challenges such as identifying grounded sequences of actions to achieve a goal, bridging the gap between high-level planning and low-level execution, and addressing the computational constraints of robotic hardware. Additionally, an essential, but frequently underestimated, aspect is the robot’s ability to recognize its own limitations and effectively delegate tasks beyond its abilities to human collaborators. Recent advancements in integrating Large Language Models (LLMs) into robotics have increased the likelihood of generating unfeasible plans, making this capability even more critical for any robot that needs to interact with a real environment. To address these challenges, this paper presents a framework that integrates open-vocabulary online grounding, planning, and embodiment, emphasizing self-awareness and self-analysis. The proposed self-awareness model enables robots to assess task complexity and map task requirements to their own capabilities. This can be used in several ways, including strategically requesting human assistance when needed. By leveraging pre-trained foundation models, our framework supports a multi-role mechanism that enhances adaptability and ensures effective collaboration between robots and humans in real-world scenarios. The experiments were conducted using a TIAGo robot with the framework’s ability to identify and delegate challenging tasks on a set of nine real-life scenarios, spanning different complexity levels. The results show a significant improvement in task success rate, achieving 89.1% compared to 37.0% of the baseline, demonstrating the effectiveness of incorporating self-awareness into the planning process. The code and the additional materials have been publicly released on the project website.

Tangible climate changes are experienced by the Earth. They refer to long-term changes in, e.g., temperature and precipitation, as well as other aspects of the Earth system. Essential Climate Variables (ECVs) are used to monitor climate changes, validate climate models and, ultimately, inform policy decisions; they are, therefore, essential markers that help scientists understand the Earth system dynamics. Fundamental contributions to the understanding and characterization of ECV values can be given by the analysis of data produced by current and next-generation Earth observation (EO) satellites, which, among other options, can be based on microwave remote sensing. EO sensors can directly characterize bio-geophysical variables connected to ECVs, such as, e.g., the soil moisture (SM), the above-ground biomass (AGB), and the freeze-thaw (FT) dynamics. While FT dynamics are primarily relevant in northern latitudes, SM and AGB are globally significant parameters. SM, AGB, and FT are also key variables for the ESA EO science strategy and the ESA Climate Change Initiative program, mainly due to their strong relevance in understanding the global hydrological and carbon cycles. This work aims to be a comprehensive review of retrieval techniques for SM, AGB, and FT, considering data collected with passive and active microwave remote sensing systems. An overview of the methods proposed in the last decades is reported and discussed, then future perspective and current challenges are outlined. This paper targets a very broad scientific community, not necessarily engineers, but rather any scientist with interest in microwave EO and satellite remote sensing. For that reason, some basic concepts are briefly reported and discussed throughout the manuscript.

Navigating and understanding complex and unknown environments autonomously demands more than just basic perception and movement from embodied agents. Truly effective exploration requires agents to possess higher-level cognitive abilities, the ability to reason about their surroundings, and make more informed decisions regarding exploration strategies. However, traditional RL approaches struggle to balance efficient exploration and semantic understanding due to limited cognitive capabilities embedded in the small policies for the agents, leading often to human drivers when dealing with semantic exploration. In this paper, we address this challenge by presenting a novel Deep Reinforcement Learning (DRL) architecture that is specifically designed for resource efficient semantic exploration. A key methodological contribution is the integration of a Vision-Language Model (VLM) common-sense through a layered reward function. The VLM query is modeled as a dedicated action, allowing the agent to strategically query the VLM only when deemed necessary for gaining external guidance, thereby conserving resources. This mechanism is combined with a curriculum learning strategy designed to guide learning at different levels of complexity to ensure robust and stable learning. Our experimental evaluation results convincingly demonstrate that our agent achieves significantly enhanced object discovery rates and develops a learned capability to effectively navigate towards semantically rich regions. Furthermore, it also shows a strategic mastery of when to prompt for external environmental information. By demonstrating a practical and scalable method for embedding common-sense semantic reasoning with autonomous agents, this research provides a novel approach to pursuing a fully intelligent and self-guided exploration in robotics.

Coordinating a fully distributed multi-agent system (MAS) can be challenging when the communication channel has very limited capabilities in terms of sending rate and packet payload. When the MAS has to deal with active obstacles in a highly partially observable environment, the communication channel acquires considerable relevance. In this paper, we present an approach to deal with task assignments in extremely active scenarios, where tasks need to be frequently reallocated among the agents participating in the coordination process. Inspired by market-based task assignments, we introduce a novel distributed coordination method to orchestrate autonomous agents' actions efficiently in low communication scenarios. In particular, our algorithm takes into account asymmetric obstacles. While in the real world, the majority of obstacles are asymmetric, they are usually treated as symmetric ones, thus limiting the applicability of existing methods. To summarize, the presented architecture is designed to tackle scenarios where the obstacles are active and asymmetric, the communication channel is poor and the environment is partially observable. Our approach has been validated in simulation and in the real world, using a team of NAO robots during official RoboCup competitions. Experimental results show a notable reduction in task overlaps in limited communication settings, with a decrease of 52% in the most frequent reallocated task.