Browsing by Author "Xie, Zhaoming"

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    ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Xie, Zhaoming; Ling, Hung Yu; Kim, Nam Hee; Panne, Michiel van de; Bender, Jan and Popa, Tiberiu
    Humans are highly adept at walking in environments with foot placement constraints, including stepping-stone scenarios where footstep locations are fully constrained. Finding good solutions to stepping-stone locomotion is a longstanding and fundamental challenge for animation and robotics. We present fully learned solutions to this difficult problem using reinforcement learning. We demonstrate the importance of a curriculum for efficient learning and evaluate four possible curriculum choices compared to a non-curriculum baseline. Results are presented for a simulated humanoid, a realistic bipedal robot simulation and a monster character, in each case producing robust, plausible motions for challenging stepping stone sequences and terrains.
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    Flexible Motion Optimization with Modulated Assistive Forces
    (ACM, 2021) Kim, Nam Hee; Ling, Hung Yu; Xie, Zhaoming; Panne, Michiel Van De; Narain, Rahul and Neff, Michael and Zordan, Victor
    Animated motions should be simple to direct while also being plausible. We present a flexible keyframe-based character animation system that generates plausible simulated motions for both physically-feasible and physically-infeasible motion specifications. We introduce a novel control parameterization, optimizing over internal actions, external assistive-force modulation, and keyframe timing. Our method allows for emergent behaviors between keyframes, does not require advance knowledge of contacts or exact motion timing, supports the creation of physically impossible motions, and allows for near-interactive motion creation. The use of a shooting method allows for the use of any black-box simulator. We present results for a variety of 2D and 3D characters and motions, using sparse and dense keyframes. We compare our control parameterization scheme against other possible approaches for incorporating external assistive forces.
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    Hierarchical Planning and Control for Box Loco-Manipulation
    (ACM Association for Computing Machinery, 2023) Xie, Zhaoming; Tseng, Jonathan; Starke, Sebastian; Panne, Michiel van de; Liu, C. Karen; Wang, Huamin; Ye, Yuting; Victor Zordan
    Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We present a physically-simulated human capable of solving box rearrangement tasks, which requires a combination of both skills. We propose a hierarchical control architecture, where each level solves the task at a different level of abstraction, and the result is a physics-based simulated virtual human capable of rearranging boxes in a cluttered environment. The control architecture integrates a planner, diffusion models, and physics-based motion imitation of sparse motion clips using deep reinforcement learning. Boxes can vary in size, weight, shape, and placement height. Code and trained control policies are provided.