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   https://doi.org/10.1109/LRA.2024.3360892  

   Shanthi, Mohanraj Devendran ; Hermans, Tucker ( March 2024 , IEEE Robotics and Automation Letters)
2. Planning Habit: Daily Planning Prompts with Alexa

   https://doi.org/10.1007/978-3-030-79460-6_7  

   Cuadra, Andrea ; Bankole, Oluseye ; Sobolev, Michael ( June 2021 , Persuasive Technology (PERSUASIVE 2021))

   Ali, Raian ; Lugrin, Birgit ; Charles, Fred (Ed.)

   The widespread adoption of intelligent voice assistants (IVAs), like Amazon’s Alexa or Google’s Assistant, presents new opportunities for designers of persuasive technologies to explore how to support people’s behavior change goals and habits with voice technology. In this work, we explore how to use planning prompts, a technique from behavior science to make specific and effective plans, with IVAs. We design and conduct usability testing (N = 13) on a voice app called Planning Habit that encourages users to formulate daily plans out loud. We identify strategies that make it possible to successfully adapt planning prompts to voice format. We then conduct a week-long online deployment (N = 40) of the voice app in the context of daily productivity. Overall, we find that traditional forms of planning prompts can be adapted to and enhanced by IVA technology. 

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3. Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

   https://doi.org/10.1109/IROS47612.2022.9981599  

   Ren, Kejia ; Kavraki, Lydia E. ; Hang, Kaiyu ( October 2022 , IEEE)
4. Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

   Ren, Kejia ; Kavraki, Lydia E. ; Hang, Kaiyu ( October 2022 , Proceedings of the International Conference on Intelligent Robots and Systems)

   Robot manipulation in cluttered environments of-ten requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets. 

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5. Multispecies genetic objectives in spatial conservation planning: Multispecies Genetic Planning

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