A Hierarchy of Reactive Behaviors Handles Complexity
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Synthesizing animations of human manipulation tasks
ACM SIGGRAPH 2004 Papers
Behavior planning for character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Variable Level-Of-Detail Motion Planning in Environments with Poorly Predictable Bodies
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Simulation-based temporal projection of everyday robot object manipulation
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Motion planning for mobile agents, such as robots, acting in the physical world is a challenging task, which traditionally concerns safe obstacle avoidance. We are interested in physics-based planning beyond collision-free navigation goals, in which the agent also needs to achieve its goals, including purposefully manipulate non-actuated bodies, in environments that contain multiple physically interacting bodies with varying degrees of controllability. Physics-based planning is computationally hard due to the large number of continuous motion actions and to the difficulty in accurately modeling the rich interactions of such controlled, manipulatable, and uncontrolled, potentially adversarial, bodies. We contribute an efficient physics-based planning algorithm that uses the agent's high-level behaviors to reduce its motion action space. We first discuss the general physics-based planning problem. We then introduce Tactics and Skills as a model for infusing goal-driven, higher level behaviors into a randomized motion planner. We present a physics-based state and transition model that employs rigid body simulations to approximate real-world interbody-dynamics. We introduce and compare two variations of our tactics-driven, physics-based planning algorithm, namely Behavioral Kinodynamic Balanced Growth Trees and Behavioral Kinodynamic Rapidly-Exploring Random Trees. We tested our physics-based planners in a variety of rich domains and show results in simulated domains where the agent manipulates an object in a dynamic non-adversarial and adversarial environment, namely in a robot minigolf and robot soccer domain, respectively.