Recent works in the domain of Human-Robot Motion (HRM) attempted to plan collision avoidance behavior that accounts for cooperation between agents. Cooperative collision avoidance between humans and robots should be conducted under several factors such as speed, heading and also human attention and intention. Based on some of these factors, people decide their crossing order during collision avoidance. However, whenever situations arise in which the choice crossing order is not consistent for people, the robot is forced to account for the possibility that both agents will assume the same role i.e. a decision detrimental to collision avoidance. In our work we evaluate the boundary that separates the decision to avoid collision as first or last crosser. Approximating the uncertainty around this boundary allows our collision avoidance strategy to address this problem based on the insight that the robot should plan its collision avoidance motion in such a way that, even if agents, at first, incorrectly choose the same crossing order, they would be able to unambiguously perceive their crossing order on their following collision avoidance action.

Recent works in the area of human robot motion showed that behaving in a human-like manner allows a robot to reduce global cognitive effort for people in the environment. Given that collision avoidance situations between people are solved cooperatively, this work models the manner in which this cooperation is done so that a robot can replicate their behavior. To that end, hundreds of situations where two walkers have crossing trajectories were analyzed. Based on these human trajectories involving a collision avoidance task, we determined how total effort is shared between each walker depending on several factors of the interaction such as crossing angle, time to collision and speed. To validate our approach, a proof of concept is integrated into ROS with Reciprocal Velocity Objects (RVO) in order to distribute collision avoidance effort in a human-like way.

This paper presents a study performed in virtual reality on the effect of gaze interception during collision avoidance between two walkers. In such a situation, mutual gaze can be considered as a form of nonverbal communication. Additionally, gaze is believed to detail future path intentions and to be part of the nonverbal negotiation to achieve avoidance collaboratively. We considered an avoidance task between a real subject and a virtual human character and studied the influence of the character's gaze direction on the avoidance behaviour of the participant. Virtual reality provided an accurate control of the situation: seventeen participants were immersed in a virtual environment, instructed to navigate across a virtual space using a joystick and to avoid a virtual character that would appear from either side. The character would either gaze or not towards the participant. Further, the character would either perform or not a reciprocal adaptation of its trajectory to avoid a potential collision with the participant. The findings of this paper were that during an orthogonal collision avoidance task, gaze behaviour did not influence the collision avoidance behaviour of the participants. Further, the addition of reciprocal collision avoidance with gaze did not modify the collision behaviour of participants. These results suggest that for the duration of interaction in such a task, body motion cues were sufficient for coordination and regulation. We discuss the possible exploitation of these results to improve the design of virtual characters for populated virtual environments and user interaction.

Daily activities require agents to interact with each other, such as during collision avoidance. The nature of visual information that is used for a collision free interaction requires further understanding. We aim to manipulate the nature of visual information in two forms, global and local information appearances. Sixteen healthy participants navigated towards a target in an immersive computer-assisted virtual environment (CAVE) using a joystick. A moving passive obstacle crossed the participant's trajectory perpendicularly at various pre-defined risks of collision distances. The obstacle was presented with one of five virtual appearances, associated to global motion cues (i.e., a cylinder or a sphere), or local motion cues (i.e., only the legs or the trunk). A full body virtual walker, showing both local and global motion cues, used as a reference condition. The final crossing distance was affected by the global motion appearances, however, appearance had no qualitative effect on motion adaptations. These findings contribute towards further understanding what information people use when interacting with others.

Annotating unlabeled motion captures plays an important role in Computer Animation for motion analysis and motion edition purposes. Locomotion is a difficult case study as all the limbs of the human body are involved whereas a low-dimensional global motion is performed. The oscillatory nature of the locomotion makes difficult the distinction between straight steps and turning ones, especially for subtle orientation changes. In this paper we propose to geometrically model the center of mass trajectory during locomotion as a C-continuous circular arcs sequence. Our model accurately analyzes the global motion into the velocity-curvature space. An experimental study demonstrates that an invariant law links curvature and velocity during straight walk. We finally illustrate how the resulting law can be used for annotation purposes: any unlabeled motion captured walk can be transformed into an annotated sequence of straight and turning steps. Several examples demonstrate the robustness of our approach and give comparison with classical threshold-based techniques. Copyright © 2011 John Wiley & Sons, Ltd.

Visual servoing based upon geometrical features such as image points coordinates is now well set on. Nevertheless, this approach has the drawback that it usually needs visual marks on the observed object to retrieve geometric features. The idea developed here is that these features can be retrieved by integrating dynamic ones, which can be estimated without any a priori knowledge of the scene. Thus, more realistic scenes can be used to achieve vision-based control such as positioning tasks. We show that an affine model is insufficient to ensure convergence and that a quadratic one is needed. Finally, results are presented related to the positioning with respect to a complex scene.

Efficient real-time robotic tasks using a monocular vision system were previously developed with simple objects (e.g. white points on a black background), within a visual servoing context. Due to recent developments, it is now possible to design real-time visual tasks exploiting motion information in the image, estimated by robust algorithms. This paper proposes such an approach to track complex objects, such as a pedestrian. It consists in integrating the measured 2D motion of the object to recover its 2D-position in the image. The principle of the tracking task is to control the camera pan and tilt such that the estimated center of the object appears at the center of the image. Real-time experimental results demonstrate the efficiency and the robustness of the method.

Visual servoing based upon geometrical features such as image point coordinates is now well studied. However, this approach has a drawback that it usually needs visual markings on the observed object to retrieve geometric features. The idea developed here is that these features can be retrieved by integrating dynamic ones, which can be estimated without any a priori knowledge of the scene. With this approach, more realistic objects can be used to achieve vision-based control such as tracking and fixation tasks. We detail the control laws concerning these two tasks, first using integration of speed in the image and then by direct regulation of these dynamic parameters. Results are finally presented and comparisons are made between the two types of control methods.

Visual servoing is generally based upon geometrical features. Recent developments were made in the way of a generalization of this approach to dynamic features. The idea is that velocity in the image can be measured without the constraint of having an a priori knowledge of the scene. In this paper, a control law to position a camera mounted on the end effector of a robot, in such a way the image plane becomes parallel to a planar object, is presented. A dynamic visual servoing approach is used by defining a control loop upon the second order spatial derivatives of the optical flow. A fixating task, which guarantees the object stays in the camera field of view is joined to the first one. Then, results obtained on a 6-DOF robot are laid out for the two studied tasks.