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1. A Spatio-Temporal Prediction and Planning Framework for Proactive Human–Robot Collaboration

   https://doi.org/10.1115/1.4063502  

   Flowers, Jared; Wiens, Gloria (December 2023, Journal of Manufacturing Science and Engineering)

   Pai Zheng (Ed.)

   Abstract A significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption. 

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2. Map Space Belief Prediction for Manipulation-Enhanced Mapping

   Marques, Joao_Marcos_Correia; Dengler, Nils; Zaenker, Tobias; Mucke, Jesper; Wang, Shenlong; Bennewitz, Maren; Hauser, Kris (June 2025, Robotics Science and Systems)

   Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Although Partially Observable Markov Decision Processes~(POMDPs) are standard for decision-making under uncertainty, representing unstructured interactive worlds remains challenging in this formalism. To tackle this, we define a POMDP whose belief is summarized by a metric-semantic grid map and propose a novel framework that uses neural networks to perform map-space belief updates to reason efficiently and simultaneously about object geometries, locations, categories, occlusions, and manipulation physics. Further, to enable accurate information gain analysis, the learned belief updates should maintain calibrated estimates of uncertainty. Therefore, we propose Calibrated Neural-Accelerated Belief Updates (CNABUs) to learn a belief propagation model that generalizes to novel scenarios and provides confidence-calibrated predictions for unknown areas. Our experiments show that our novel POMDP planner improves map completeness and accuracy over existing methods in challenging simulations and successfully transfers to real-world cluttered shelves in zero-shot fashion. 

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3. FPGA-Based Velocity Estimation for Control of Robots with Low-Resolution Encoders

   Wu, Jie Ying; Chen, Zihan; Deguet, Anton; Kazanzides, Peter (October 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Robot control algorithms often rely on measurements of robot joint velocities, which can be estimated by measuring the time between encoder edges. When encoder edges occur infrequently, such as at low velocities and/or with low resolution encoders, this measurement delay may affect the stability of closed-loop control. This is evident in both the joint position control and Cartesian impedance control of the da Vinci Research Kit (dVRK), which contains several low-resolution encoders. We present a hardware-based method that gives more frequent velocity updates and is not affected by common encoder imperfections such as non-uniform duty cycles and quadrature phase error. The proposed method measures the time between consecutive edges of the same type but, unlike prior methods, is implemented for the rising and falling edges of both channels. Additionally, it estimates acceleration to enable software compensation of the measurement delay. The method is shown to improve Cartesian impedance control of the dVRK. 

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4. Bayesian History Matching Applied to the Calibration of a Gravity Wave Parameterization

   https://doi.org/10.1029/2023MS004163  

   King, Robert C.; Mansfield, Laura A.; Sheshadri, Aditi (April 2024, Journal of Advances in Modeling Earth Systems)

   Abstract Breaking atmospheric gravity waves (GWs) in the tropical stratosphere are essential in driving the roughly 2‐year oscillation of zonal winds in this region known as the Quasi‐Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial GW stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History matching & Ensemble Kalman Inversion (EKI). History matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, history matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes. 

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5. Learning From Human Directional Corrections

   https://doi.org/10.1109/TRO.2022.3190221  

   Jin, Wanxin; Murphey, Todd D.; Lu, Zehui; Mou, Shaoshuai (February 2023, IEEE Transactions on Robotics)

   This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitude corrections; since a human needs to carefully choose the magnitude of each correction, those methods can easily lead to over-corrections and learning inefficiency. The proposed method only requires human directional corrections — corrections that only indicate the direction of an input change without indicating its magnitude. We only assume that each correction, regardless of its magnitude, points in a direction that improves the robot’s current motion relative to an unknown objective function. The allowable corrections satisfying this assumption account for half of the input space, as opposed to the magnitude corrections which have to lie in a shrinking level set. For each directional correction, the proposed method updates the estimate of the objective function based on a cutting plane method, which has a geometric interpretation. We have established theoretical results to show the convergence of the learning process. The proposed method has been tested in numerical examples, a user study on two human-robot games, and a real-world quadrotor experiment. The results confirm the convergence of the proposed method and further show that the method is significantly more effective (higher success rate), efficient/effortless (less human corrections needed), and potentially more accessible (fewer early wasted trials) than the state-of-the-art robot learning frameworks. 

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