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Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential. Accurately characterizing the neurophysiology of surgeons during intraoperative error will help guide more accurate performance assessment and precision training for surgeons and other teleoperators. To better understand the neurophysiology of intraoperative error, we build and deploy a system for intraoperative error detection and electroencephalography (EEG) signal synchronization during robot-assisted surgery (RAS). We then examine the association between EEG data and detected errors. Our results suggest that there are significant EEG changes during intraoperative error that are detectable irrespective of surgical experience level.

Robots tasked with object assembly by manipulation of parts require not only a high-level plan for order of placement of parts but also detailed low-level information on how to place and pick the part based on its state. This is a complex multi-level problem prone to failures at various levels. This paper employs meta reasoning architecture along with robotics principles and proposes dual encoding of state expectations during the progression of task to ground nominal scenarios. We present our results on table-top scenario using perceptual expectations based in the concept of occupancy grids and key point representations. Our results in a constrained manipulation setting suggest using low-level information or high-level expectations alone the system performs worse than if the architecture uses them both. We then outline a complete architecture and system which tackles this problem for repairing more generic assembly plans with objects moving in spaces with 6 degrees of freedom.

Statistical learning techniques and increased computational power have facilitated the development of self-driving car technology. However, a limiting factor has been the high expense of scaling and maintaining high-definition (HD) maps. These maps are a crucial backbone for many approaches to self-driving technology. In response to this challenge, we present an approach that fuses pre-built point cloud map data with images to automatically and accurately identify static landmarks such as roads, sidewalks, and crosswalks. Our pipeline utilizes semantic segmentation of 2D images, associates semantic labels with points in point cloud maps to pinpoint locations in the physical world, and employs a confusion matrix formulation to generate a probabilistic bird’s-eye view semantic map from semantic point clouds. The approach has been tested in an urban area with different segmentation networks to generate a semantic map with road features. The resulting map provides a rich context of the environment that is valuable for downstream tasks such as trajectory generation and intent prediction. Moreover, it has the potential to be extended to the automatic generation of HD maps for semantic features. The entire software pipeline is implemented in the robot operating system (ROS), a widely used robotics framework, and made available.

This work explores methodologies for dynamic trajectory generation for urban driving environments by utilizing coarse global plan representations. In contrast to state-of-the-art architectures for autonomous driving that often leverage lane-level high-definition (HD) maps, we focus on minimizing required map priors that are needed to navigate in dynamic environments that may change over time. To incorporate high-level instructions (i.e., turn right vs. turn left at intersections), we compare various representations provided by lightweight and open-source OpenStreetMaps (OSM) and formulate a conditional generative model strategy to explicitly capture the multimodal characteristics of urban driving. To evaluate the performance of the models introduced, a data collection phase is performed using multiple full-scale vehicles with ground truth labels. Our results show potential use cases in dynamic urban driving scenarios with real-time constraints. The dataset is released publicly as part of this work in combination with code and benchmarks.

The paper presents an HRI architecture for human-augmented mapping, which has been implemented and tested on an autonomous mobile robotic platform. Through interaction with a human, the robot can augment its autonomously acquired metric map with qualitative information about locations and objects in the environ- ment. The system implements various interaction strategies observed in independently performed Wizard-of-Oz studies. The paper discusses an ontology-based approach to multi-layered conceptual spatial mapping that pro- vides a common ground for human-robot dialogue. This is achieved by combining acquired knowledge with innate conceptual commonsense knowledge in order to infer new knowledge. The architecture bridges the gap between the rich semantic representations of the meaning expressed by verbal utterances on the one hand and the robot?s internal sensor-based world representation on the other. It is thus possible to establish references to spatial areas in a situated dialogue between a human and a robot about their environment. The resulting conceptual descrip- tions represent qualitative knowledge about locations in the environment that can serve as a basis for achieving a notion of situational awareness.

The paper presents an HRI architecture for human-augmented mapping, which has been implemented and tested on an autonomous mobile robotic platform. Through interaction with a human, the robot can augment its autonomously acquired metric map with qualitative information about locations and objects in the environment. The system implements various interaction strategies observed in independently performed Wizard-of-Oz studies. The paper discusses an ontology-based approach to multi-layered conceptual spatial mapping that provides a common ground for human-robot dialogue. This is achieved by combining acquired knowledge with innate conceptual commonsense knowledge in order to infer new knowledge. The architecture bridges the gap between the rich semantic representations of the meaning expressed by verbal utterances on the one hand and the robot's internal sensor-based world representation on the other. It is thus possible to establish references to spatial areas in a situated dialogue between a human and a robot about their environment. The resulting conceptual descriptions represent qualitative knowledge about locations in the environment that can serve as a basis for achieving a notion of situational awareness.