We have equipped a Honda ASIMO humanoid with the ability to navigate autonomously in obstacle-filled environments. In addition to finding its way through known, fixed obstacle configurations, the planning system can reason about the future state of the world to locomote through challenging environments when the obstacle motions can be inferred from observation. This video presents work using a vision system to predict the velocities of objects in the scene, allowing ASIMO to safely navigate autonomously through a dynamic environment. Neither obstacle positions nor velocities are known at the start of the trial, but are estimated online as the robot walks. The planner constantly adjusts the footstep path with the latest estimates of ASIMO’s position and the obstacle trajectories, allowing the robot to successfully circumnavigate the moving obstacles.

For humanoid robots to fully realize their biped potential in a three-dimensional world and step over, around or onto obstacles such as stairs, appropriate and efficient approaches to execution, planning and perception are required. To this end, we have accelerated a robust model-based three-dimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot. The tracker recovers the full 6 degree-of- freedom pose of viewable objects relative to the robot. Leveraging the computational resources of the GPU for perception has enabled us to increase our tracker's robustness to the significant camera displacement and camera shake typically encountered during humanoid navigation. We have combined our approach with a footstep planner and a controller capable of adaptively adjusting the height of swing leg trajectories. The resulting integrated perception-planning-action system has allowed an HRP-2 humanoid robot to successfully and rapidly localize, approach and climb stairs, as well as to avoid obstacles during walking.

We present an approach to motion planning for highly articulated systems that aims to ensure robust execution by augmenting the planning process to reason about the robot's ability to successfully perceive its environment during operation. By simulating the robot's perception system during search, our planner generates a metric, the so-called perceptive capability, that quantifies the 'sensability' of the environment in each state given the task to be accomplished. We have applied our method to the problem of planning robust autonomous manipulations as performed by a humanoid robot in a kitchen environment. Our results indicate that reasoning about the future perceptive capability has the potential to greatly facilitate any task requiring visual feedback during control of the robot manipulator and can thus ensure higher task success rates than perception-unaware planning.

We present the concept of an "intelligent" joystick, an architecture which provides simple and intuitive high-level directional control of a legged robot while adjusting the actual foot placements autonomously to avoid stepping in undesirable places. The general concept can be likened to riding a horse: high-level commands are provided, while the "intelligence" of the underlying system selects proper foot placements with respect to the shape and properties of the underlying terrain and overall balance considerations. We demonstrate a prototype system used for realtime control of the humanoid robot HRP-2

As navigation autonomy becomes an increasingly important research topic for biped humanoid robots, efficient approaches to perception and mapping that are suited to the unique characteristics of humanoids and their typical operating environments are required. This paper presents a system for online environment reconstruction that utilizes both external sensors for global localization, and on-body sensors for detailed local mapping. An external optical motion capture system is used to accurately localize on-board sensors that integrate successive 2D views of a calibrated camera and range measurements from a SwissRanger SR-2 time-of-flight sensor to construct global environment maps in real-time. Environment obstacle geometry is encoded in 2D occupancy grids and 2.5D height maps for navigation planning. We present an on-body implementation for the HRP-2 humanoid robot that, combined with a footstep planner, enables the robot to autonomously traverse dynamic environments containing unpredictably moving obstacles

In this paper, we introduce Roillo, a social robotic platform for investigating, in the context of children's playrooms, questions about how to design compelling nonverbal interactive behaviors for social robots. Specifically, we are interested in the importance of rhythm to natural interactions and its role in the expression of affect, attention, and intent. Our design process has consisted of rendering, animation, surveys, mechanical prototyping, and puppeteered interaction with children

We present a method for allowing a humanoid robot to recognize its own motion in its visual field, thus enabling it to distinguish itself from other agents in the vicinity. Our approach consists of learning a characteristic time window between the initiation of motor movement and the perception of arm motions. The method has been implemented and evaluated on an infant humanoid platform. Our results demonstrate the effectiveness of using the delayed temporal contingency in the action-perception loop as a basis for simple self-other discrimination. We conclude by suggesting potential applications in social robotics and in generating forward models of motion.

Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, we present a real time approach to emotion recognition through facial expression in live video. We employ an automatic facial feature tracker to perform face localization and feature extraction. The facial feature displacements in the video stream are used as input to a Support Vector Machine classifier. We evaluate our method in terms of recognition accuracy for a variety of interaction and classification scenarios. Our person-dependent and person-independent experiments demonstrate the effectiveness of a support vector machine and feature tracking approach to fully automatic, unobtrusive expression recognition in live video. We conclude by discussing the relevance of our work to affective and intelligent man-machine interfaces and exploring further improvements.