Open Access

The success of any agent, human or artificial, ultimately depends on their successfully accomplishing the given goals. Agents may, however, fail to do so for many reasons. With artificial agents, such as robots, this may be due to internal faults or exogenous events in the complex, dynamic environments in which they operate. The bottom line is that plans, even good ones, can fail. Despite decades of research, effective methods for artificial agents to cope with plan failure remain limited and are often impractical in the real world. One common reason for failure that plagues agents, human and artificial alike, is that objects that are expected to be used to get the job done are often found to be missing or unavailable. Humans might, with little effort, accomplish their tasks by making substitutions. When they are not sure if an object is available, they may even proceed optimistically and switch to making a substitution when they confirm that an object is indeed unavailable. In this work, the system uses Description Logics to enable open-world reasoning --- making it possible to distinguish between cases where an object is missing/unavailable and cases where the failure to even generate a plan is due to the planner's use of the closed-world assumption (where the fact stating that something is true is missing from its knowledge base and so it is assumed to be not true). This ability to distinguish between something being missing and having incomplete information enables the agent to behave intelligently: recognising whether it should identify and then plan with a suitable substitute or create a placeholder, in the case of incomplete information. By representing the functional affordances of objects (i.e. what they are meant to be used for), socially-expected and accepted object substitutions are made possible. The system also uses the Conceptual Spaces approach to provide feature-based similarity measures that make the given task a first-class citizen in the identification of a suitable substitute. The generation of plans to `get the job done' is made possible by incorporating the Hierarchical Task Network planning approach. It is combined with a robust execution/monitoring system and contributes to the success of the robot in achieving its goals.

Unmanned Aerial Vehicles (UAVs) are increasingly used for reforestation and forest monitoring, including seed dispersal in hard-to-reach terrains. However, a detailed understanding of the forest floor remains a challenge due to high natural variability, quickly changing environmental parameters, and ambiguous annotations due to unclear definitions. To address this issue, we adapt the Segment Anything Model (SAM), a vision foundation model with strong generalization capabilities, to segment forest floor objects such as tree stumps, vegetation, and woody debris. To this end, we employ parameter-efficient fine-tuning (PEFT) to fine-tune a small subset of additional model parameters while keeping the original weights fixed. We adjust SAM's mask decoder to generate masks corresponding to our dataset categories, allowing for automatic segmentation without manual prompting. Our results show that the adapter-based PEFT method achieves the highest mean intersection over union (mIoU), while Low-rank Adaptation (LoRA), with fewer parameters, offers a lightweight alternative for resource-constrained UAV platforms.

This paper presents dCTIDH, a CSIDH implementation that combines two recent developments into a novel state-of-the-art deterministic implementation. We combine the approach of deterministic variants of CSIDH with the batching strategy of CTIDH, which shows that the full potential of this key space has not yet been explored. This high-level adjustment in itself leads to a significant speed-up. To achieve an effective deterministic evaluation in constant time, we introduce Wombats, a new approach to performing isogenies in batches, specifically tailored to the behavior required for deterministic CSIDH using CTIDH batching. Furthermore, we explore the two-dimensional space of optimal primes for dCTIDH, with regard to both the performance of dCTIDH in terms of finite-field operations per prime and the efficiency of finite-field operations, determined by the prime shape, in terms of cycles. This allows us to optimize both for choice of prime and scheme parameters simultaneously. Lastly, we implement and benchmark constant-time, deterministic dCTIDH. Our results show that dCTIDH not only outperforms state-of-the-art deterministic CSIDH, but even non-deterministic CTIDH: dCTIDH-2048 is faster than CTIDH-2048 by 17 percent, and is almost five times faster than dCSIDH-2048.

This specification defines a method for computing a hash value over a CBOR Object Signing and Encryption (COSE) Key. It specifies which fields within the COSE Key structure are included in the cryptographic hash computation, the process for creating a canonical representation of these fields, and how to hash the resulting byte sequence. The resulting hash value, referred to as a "thumbprint", can be used to identify or select the corresponding key.

This dataset contains multimodal data recorded on robots performing robot-to-human and human-to-robot handovers. The intended application of the dataset is to develop and benchmark methods for failure detection during the handovers. Thus the trials in the dataset contain both successful and failed handover actions. For a more detailed description of the dataset, please see the included Datasheet.

This is the YOLO format version of the robocup2023 competition dataset. The dataset is annotated using the Anylabeling tool (Link: https://github.com/vietanhdev/anylabeling).

Grading student answers and providing feedback are essential yet time-consuming tasks for educators. Recent advancements in Large Language Models (LLMs), including ChatGPT, Llama, and Mistral, have paved the way for automated support in this domain. This paper investigates the efficacy of instruction-following LLMs in adhering to predefined rubrics for evaluating student answers and delivering meaningful feedback. Leveraging the Mohler dataset and a custom German dataset, we evaluate various models, from commercial ones like ChatGPT to smaller open-source options like Llama, Mistral, and Command R. Additionally, we explore the impact of temperature parameters and techniques such as few-shot prompting. Surprisingly, while few-shot prompting enhances grading accuracy closer to ground truth, it introduces model inconsistency. Furthermore, some models exhibit non-deterministic behavior even at near-zero temperature settings. Our findings highlight the importance of rubrics in enhancing the interpretability of model outputs and fostering consistency in grading practices.

Altering posture relative to the direction of gravity, or exposure to microgravity has been shown to affect many aspects of perception, including size perception. Our aims in this study were to investigate whether changes in posture and long-term exposure to microgravity bias the visual perception of object height and to test whether any such biases are accompanied by changes in precision. We also explored the possibility of sex/gender differences. Two cohorts of participants (12 astronauts and 20 controls, 50% women) varied the size of a virtual square in a simulated corridor until it was perceived to match a reference stick held in their hands. Astronauts performed the task before, twice during, and twice after an extended stay onboard the International Space Station. On Earth, they performed the task of sitting upright and lying supine. Earth-bound controls also completed the task five times with test sessions spaced similarly to the astronauts; to simulate the microgravity sessions on the ISS they lay supine. In contrast to earlier studies, we found no immediate effect of microgravity exposure on perceived object height. However, astronauts robustly underestimated the height of the square relative to the haptic reference and these estimates were significantly smaller 60 days or more after their return to Earth. No differences were found in the precision of the astronauts’ judgments. Controls underestimated the height of the square when supine relative to sitting in their first test session (simulating Pre-Flight) but not in later sessions. While these results are largely inconsistent with previous results in the literature, a posture-dependent effect of simulated eye height might provide a unifying explanation. We were unable to make any firm statements related to sex/gender differences. We conclude that no countermeasures are required to mitigate the acute effects of microgravity exposure on object height perception. However, space travelers should be warned about late-emerging and potentially long-lasting changes in this perceptual skill.

Few mobile robot developers already test their software on simulated robots in virtual environments or sceneries. However, the majority still shy away from simulation-based test campaigns because it remains challenging to specify and execute suitable testing scenarios, that is, models of the environment and the robots’ tasks. Through developer interviews, we identified that managing the enormous variability of testing scenarios is a major barrier to the application of simulation-based testing in robotics. Furthermore, traditional CAD or 3D-modelling tools such as SolidWorks, 3ds Max, or Blender are not suitable for specifying sceneries that vary significantly and serve different testing objectives. For some testing campaigns, it is required that the scenery replicates the dynamic (e.g., opening doors) and static features of real-world environments, whereas for others, simplified scenery is sufficient. Similarly, the task and mission specifications used for simulation-based testing range from simple point-to-point navigation tasks to more elaborate tasks that require advanced deliberation and decision-making. We propose the concept of composable and executable scenarios and associated tooling to support developers in specifying, reusing, and executing scenarios for the simulation-based testing of robotic systems. Our approach differs from traditional approaches in that it offers a means of creating scenarios that allow the addition of new semantics (e.g., dynamic elements such as doors or varying task specifications) to existing models without altering them. Thus, we can systematically construct richer scenarios that remain manageable. We evaluated our approach in a small simulation-based testing campaign, with scenarios defined around the navigation stack of a mobile robot. The scenarios gradually increased in complexity, composing new features into the scenery of previous scenarios. Our evaluation demonstrated how our approach can facilitate the reuse of models and revealed the presence of errors in the configuration of the publicly available navigation stack of our SUT, which had gone unnoticed despite its frequent use.