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1. Social Zone as a Barrier Function for Socially-Compliant Robot Navigation

   Jang, J; Ghaffari, M (October 2024, arXiv preprint arXiv:2405.15101)

   arXiv (Ed.)

   This study addresses the challenge of integrating social norms into robot navigation, which is essential for ensuring that robots operate safely and efficiently in human-centric environments. Social norms, often unspoken and implicitly understood among people, are difficult to explicitly define and implement in robotic systems. To overcome this, we derive these norms from real human trajectory data, utilizing the comprehensive ATC dataset to identify the minimum social zones humans and robots must respect. These zones are integrated into the robot’s navigation system by applying barrier functions, ensuring the robot consistently remains within the designated safety set. Simulation results demonstrate that our system effectively mimics human-like navigation strategies, such as passing on the right side and adjusting speed or pausing in constrained spaces. The proposed framework is versatile, easily comprehensible, and tunable, demonstrating the potential to advance the development of robots designed to navigate effectively in human-centric environments. 

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2. Finite-Horizon Synthesis for Probabilistic Manipulation Domains

   https://doi.org/10.1109/ICRA48506.2021.9561297  

   Wells, Andrew M.; Kingston, Zachary; Lahijanian, Morteza; Kavraki, Lydia E.; Vardi, Moshe Y. (May 2021, International Conference on Robotics and Automation - ICRA)

   null (Ed.)

   Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of human moves. The limit on human moves is unintuitive, and in many settings determinism is undesirable. In this paper, we present a novel planning method for collaborative human-robot manipulation tasks via probabilistic synthesis. We introduce a probabilistic manipulation domain that captures the interaction by allowing for both robot and human actions with states that represent the configurations of the objects in the workspace. The task is specified using Linear Temporal Logic over finite traces (LTLf ). We then transform our manipulation domain into a Markov Decision Process (MDP) and synthesize an optimal policy to satisfy the specification on this MDP. We present two novel contributions: a formalization of probabilistic manipulation domains allowing us to apply existing techniques and a comparison of different encodings of these domains. Our framework is validated on a physical UR5 robot. 

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3. A Humanoid Robot Object Perception Approach Using Depth Images

   https://doi.org/10.1109/NAECON46414.2019.9057808  

   Cofield, Aaron; El-Shair, Zaid; Rawashdeh, Samir A. (January 2019, Proceedings of IEEE National Aerospace and Electronics Conference (NAECON))

   null (Ed.)

   Humanoid robots have had significant research interest in the past two decades. Their classification as mobile manipulators allows them to work in unstructured environments creating new possibilities for human-robot interaction. Object grasping and manipulation are essential and enabling capabilities for mobile humanoid robots that require reliable perception. This paper presents a perception approach using depth images from an RGB-D camera to estimate the work plane and estimate object positions relative to the robot. Results from experiments with a set of object shapes and scenarios are presented. 

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4. A Holistic Approach to Human-Supervised Humanoid Robot Operations in Extreme Environments

   https://doi.org/10.3389/frobt.2021.550644  

   Wonsick, Murphy; Long, Philip; Önol, Aykut Özgün; Wang, Maozhen; Padır, Taşkın (June 2021, Frontiers in Robotics and AI)

   Nuclear energy will play a critical role in meeting clean energy targets worldwide. However, nuclear environments are dangerous for humans to operate in due to the presence of highly radioactive materials. Robots can help address this issue by allowing remote access to nuclear and other highly hazardous facilities under human supervision to perform inspection and maintenance tasks during normal operations, help with clean-up missions, and aid in decommissioning. This paper presents our research to help realize humanoid robots in supervisory roles in nuclear environments. Our research focuses on National Aeronautics and Space Administration (NASA’s) humanoid robot, Valkyrie, in the areas of constrained manipulation and motion planning, increasing stability using support contact, dynamic non-prehensile manipulation, locomotion on deformable terrains, and human-in-the-loop control interfaces. 

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5. IHSR: A Framework Enables Robots to Learn Novel Hand Signals From a Few Samples

   https://doi.org/10.1109/AIM55361.2024.10637093  

   Podder, Kanchon Kanti; Zhang, Jian; Wu, Yongshuai (July 2024, IEEE)

   This project introduces a framework to enable robots to recognize human hand signals, a reliable and feasible device-free means of communication in many noisy environments such as construction sites and airport ramps, to facilitate efficient human-robot collaboration. Various hand signal systems are accepted in many small groups for specific purposes, such as Marshalling on airport ramps and construction site crane operations. Robots must be robust to unpredictable conditions, including various backgrounds and human appearances, an extreme challenge imposed by open environments. To address these challenges, we propose Instant Hand Signal Recognition (IHSR), a learning-based framework with world knowledge of human gestures embedded, for robots to learn novel hand signals in a few samples. It also offers robust zero-shot generalization to recognize learned signals in novel scenarios. Extensive experiments show that our IHSR can learn a novel hand signal in only 50 samples, which is 30+ times more efficient than the state-of-the-art method. It also demonstrates a robust zero-shot generalization for deploying a learned model in unseen environments to recognize hand signals from unseen human users. 

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