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Carnegie Mellon Robotics Institute

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Abstract

In this paper we demonstrate a method for fine-grained modeling of a synthetic agent's physical capabilities----running, jumping, sneaking, and other modes of movement. Using motion capture data acquired from human subjects, we extract a motion graph and construct a cost map for the space of agent actions. We show how a planner can incorporate this cost model into the planning process to select between equivalent goal-achieving plans. We explore the utility of our model in three different capacities: 1) modeling other agents in the environment; 2) representing heterogeneous agents with different physical capabilities; 3) modeling agent physical states (e.g., wounded or tired agents). This technique can be incorporated into applications where human-like, high-fidelity physical models are important to the agents' reasoning process, such as virtual training environments.

Keywords

synthetic agents: human-like, lifelike, and believable agents

Notes

Sponsor: ONR

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab

Associated Project(s): Agent-based Composition of Behavioral Models

Number of pages: 8

Text Reference

Gita Sukthankar, Michael Mandel, Katia Sycara, and Jessica K Hodgins, "Modeling Physical Capabilities of Humanoid Agents Using Motion Capture Data," Proceedings of Autonomous Agents and Multi-Agent Systems (AAMAS), July, 2004, pp. 344 - 351.

BibTeX Reference

@inproceedings{Sukthankar_2004_4668,
   author = "Gita Sukthankar and Michael Mandel and Katia Sycara and Jessica K Hodgins",
   title = "Modeling Physical Capabilities of Humanoid Agents Using Motion Capture Data",
   booktitle = "Proceedings of Autonomous Agents and Multi-Agent Systems (AAMAS)",
   pages = "344 - 351",
   month = "July",
   year = "2004",
}

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