Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method EXPANSION-GRR that leverages efficient configuration space projections and enables a rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.

Advancements in mobile sensing, data mining, and artificial intelligence have revolutionized the collection and analysis of Human-generated Spatial-Temporal Data (HSTD), paving the way for diverse applications across multiple domains. However, previous works have primarily focused on designing task-specific models for different problems, which lack transferability and generalizability when confronted with diverse HSTD. Additionally, these models often require a large amount of labeled data for optimal performance. While pre-trained models in Natural Language Processing (NLP) and Computer Vision (CV) domains have showcased impressive transferability and generalizability, similar efforts in the spatial-temporal data domain have been limited. In this paper, we take the lead and introduce the Spatial-Temporal Pre-Training model, $i.e$., STPT, which is connected with a self-supervised learning task, to address these limitations. STPT enables the creation of robust and versatile representations of HSTD. We validate our framework using real-world data and demonstrate its efficacy through two downstream tasks, $i.e$., trajectory classification and driving activity identification $(e.g$., identifying seeking $vs$. serving behaviors in taxi trajectories). Our results achieve an accuracy of 83.125% (16.2% higher than the average baseline) for human mobility identification and an accuracy of 77.88% (13.0% higher than the average baseline) for the human activity identification task. These outcomes underscore the potential of our pre-trained model for diverse downstream applications within the spatial-temporal data domain.

This paper presents a novel integration of a shared autonomous mobile humanoid robot for remote nursing assistance. The proposed nursing robot has a motorized versatile supporting structure to allow flexible integration of the system components, autonomously adjust its mobile manipulation workspace and improve its reachability and manipulability to operate in a cluttered environment. The robot also provides a novel integration of robot autonomy to reduce the human effort to coordinate the motorized chest and arm motion, control the precise manipulation of objects and camera viewpoint, and handle complex collision avoidance in human-guided gross manipulation. Moreover, we developed an open-source virtual testbed that integrates ROS- and Unity-based robot simulation and benchmark mobile manipulation nursing tasks and scenarios in a realistic simulation of a hospital environment. The virtual testbed supports various contemporary gaming and AR/VR interfaces to control the virtual human and robots, and provides autonomy for navigation, manipulation, and remote active perception assistance. We conducted a user study (N=9) to validate that the versatile supporting structure and shared autonomy of the physical testbed can effectively reduce the human effort to control unstructured manipulation, and improve the robot's reachability and manipulability. In addition, we conducted a pilot study (N=8) to test the usability of the virtual testbed and collect feedback from representative users.