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1. Learning Optimized Human Motion via Phase Space Analysis

   Gesel, Paul ; Mikulis-Borsoi, Francesco ; LaRoche, Dain ; Arthanat, Sajay ; Begum, Momotaz ( October 2020 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem.We assume a weighted combination of basis objective functions is the true objective function for a demonstrated motion. We derive basis objective functions analogous to those in human movement literature to optimize the robot’s motion. This method aims to naturally adapt the learned motion in different situations. To validate our approach, we learn motions from two categories: 1) commonly prescribed therapeutic exercises and 2) tea making. We show the reproduction accuracy of our method and compare torque requirements to the dynamic motion primitive for each motion, with and without an added load. 

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2. 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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3. Human-Robot Collaboration: A Predictive Collision Detection Approach for Operation Within Dynamic Environments

   https://doi.org/10.1115/ISFA2020-9659  

   Streitmatter, G.L. ; Wiens, G.J ( July 2020 , ASME International Symposium on Flexible Automation Conference)

   Robots and humans closely working together within dynamic environments must be able to continuously look ahead and identify potential collisions within their ever-changing environment. To enable the robot to act upon such situational awareness, its controller requires an iterative collision detection capability that will allow for computationally efficient Proactive Adaptive Collaboration Intelligence (PACI) to ensure safe interactions. In this paper, an algorithm is developed to evaluate a robot’s trajectory, evaluate the dynamic environment that the robot operates in, and predict collisions between the robot and dynamic obstacles in its environment. This algorithm takes as input the joint motion data of predefined robot execution plans and constructs a sweep of the robot’s instantaneous poses throughout time. The sweep models the trajectory as a point cloud containing all locations occupied by the robot and the time at which they will be occupied. To reduce the computational burden, Coons patches are leveraged to approximate the robot’s instantaneous poses. In parallel, the algorithm creates a similar sweep to model any human(s) and other obstacles being tracked in the operating environment. Overlaying temporal mapping of the sweeps reveals anticipated collisions that will occur if the robot-human do not proactively modify their motion. The algorithm is designed to feed into a segmentation and switching logic framework and provide real-time proactive-n-reactive behavior for different levels of human-robot interactions, while maintaining safety and production efficiency. To evaluate the predictive collision detection approach, multiple test cases are presented to quantify the computational speed and accuracy in predicting collisions. 

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4. Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery

   https://doi.org/10.1109/ICRA46639.2022.9812068  

   Gu, Zhaoyuan ; Boyd, Nathan ; Zhao, Ye ( May 2022 , International Conference on Robotics and Automation)

   In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot’s full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes. 

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5. Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

   https://doi.org/10.15607/RSS.2021.XVII.053  

   Sun, Muchen ; Baldini, Francesca ; Trautman, Peter ; Murphey, Todd ( July 2021 , Robotics: Science and Systems)

   null (Ed.)

   Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents’ trajectories with the planning of the robot’s trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents’ intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance. 

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