Analyzing Whole-Body Pose Transitions in Multi-Contact Motions

When executing whole-body motions, humans are able to use a large variety of support poses which not only utilize the feet, but also hands, knees and elbows to enhance stability. While there are many works analyzing the transitions involved in walking, very few works analyze human motion where more complex supports occur. In this work, we analyze complex support pose transitions in human motion involving locomotion and manipulation tasks (loco-manipulation). We have applied a method for the detection of human support contacts from motion capture data to a large-scale dataset of loco-manipulation motions involving multi-contact supports, providing a semantic representation of them. Our results provide a statistical analysis of the used support poses, their transitions and the time spent in each of them. In addition, our data partially validates our taxonomy of whole-body support poses presented in our previous work. We believe that this work extends our understanding of human motion for humanoids, with a long-term objective of developing methods for autonomous multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

Teaching humanoid robotics by means of human teleoperation through RGB-D sensors

This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels

Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semi-unstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation 201