In order to deliver information effectively, virtual human demonstrators must be able to address complex spatial constraints and at the same time replicate motion coordination patterns observed in human-human interactions. We introduce in this paper a whole-body motion planning and synthesis framework that coordinates locomotion, body positioning, action execution and gaze behavior for generic demonstration tasks among obstacles. Human-like solutions are achieved with a coordination model extracted from experiments with human subjects. Given an observer location and a target demonstration to be performed, the proposed planner automatically identifies body placements respecting visibility constraints, locomotion accessibility, and action feasibility among obstacles. Actions are modeled with clusters of example motions and a fast collision avoidance procedure in blending space is introduced to avoid nearby obstacles when needed. Locomotion towards new placements integrates planning among obstacles and is based on a motion capture database organized for efficient synthesis of motions with precise path following and arrival constraints. The proposed solution introduces effective approaches for modeling and solving complex demonstrative tasks for interactive applications.

ABSTRACT Humanoid three‐dimensional (3D) models can be easily acquired through various sources, including through online marketplaces. The use of such models within a game or simulation environment requires human input and intervention in order to associate such a model with a relevant set of motions and control mechanisms. In this paper, we demonstrate a pipeline where humanoid 3D models can be incorporated within seconds into an animation system and infused with a wide range of capabilities, such as locomotion, object manipulation, gazing, speech synthesis and lip syncing. We offer a set of heuristics that can associate arbitrary joint names with canonical ones and describe a fast retargeting algorithm that enables us to instill a set of behaviors onto an arbitrary humanoid skeleton on‐the‐fly. We believe that such a system will vastly increase the use of 3D interactive characters due to the ease that new models can be animated.Copyright © 2013 John Wiley & Sons, Ltd.

We introduce an approach for enabling sampling-based planners to compute motions with humanlike appearance. The proposed method is based on a space of blendable example motions collected by motion capture. This space is explored by a sampling-based planner that is able to produce motions around obstacles while keeping solutions similar to the original examples. The results therefore largely maintain the humanlike characteristics observed in the example motions. The method is applied to generic upper-body actions and is complemented by a locomotion planner that searches for suitable body placements for executing upper-body actions successfully. As a result, our overall multi-modal planning method is able to automatically coordinate whole-body motions for action execution among obstacles, and the produced motions remain similar to example motions given as input to the system.

Two studies investigated the nature of environmental context on various parameters of pointing. The results revealed the need for extreme temporal precision and the need for efficient algorithms to parse out different styles of pointing. Most variability in pointing came from individual differences, and a method to classify the kind of point and derive its temporal parameters is discussed. These results and methods improve the pragmatism of virtual reality, making events appear more realistic by emphasizing temporal precision.

This paper presents a multi-skill motion planner which is able to sequentially synchronize parameterized motion skills in order to achieve humanoid motions exhibiting complex whole-body coordination. The proposed approach integrates sampling-based motion planning in continuous parametric spaces with discrete search over skill choices, selecting the search strategy according to the functional type of each skill being coordinated. As a result, the planner is able to sequence arbitrary motion skills (such as reaching, balance adjustment, stepping, etc) in order to achieve complex motions needed for solving humanoid reaching tasks in realistic environments. The proposed framework is applied to the HOAP-3 humanoid robot and several results are presented.