In this paper, we propose a physics-based model of suction phenomenon to achieve simulation of deformable objects like suction cups. Our model uses a constraint-based formulation to simulate the variations of pressure inside suction cups. The respective internal pressures are represented as pressure constraints which are coupled with anti-interpenetration and friction constraints. Furthermore, our method is able to detect multiple air cavities using information from collision detection. We solve the pressure constraints based on the ideal gas law while considering several cavity states. We test our model with a number of scenarios reflecting a variety of uses, for instance, a spring loaded jumping toy, a manipulator performing a pick and place task, and an octopus tentacle grasping a soda can. We also evaluate the ability of our model to reproduce the physics of suction cups of varying shapes, lifting objects of different masses, and sliding on a slippery surface. The results show promise for various applications such as the simulation in soft robotics and computer animation.

Learning is usually performed by observing real robot executions. Physics-based simulators are a good alternative for providing highly valuable information while avoiding costly and potentially destructive robot executions. We present a novel approach for learning the probabilities of symbolic robot action outcomes. This is done leveraging different environments, such as physics-based simulators, in execution time. To this end, we propose MENID (Multiple Environment Noise Indeterministic Deictic) rules, a novel representation able to cope with the inherent uncertainties present in robotic tasks. MENID rules explicitly represent each possible outcomes of an action, keep memory of the source of the experience, and maintain the probability of success of each outcome. We also introduce an algorithm to distribute actions among environments, based on previous experiences and expected gain. Before using physics-based simulations, we propose a methodology for evaluating different simulation settings and determining the least time-consuming model that could be used while still producing coherent results. We demonstrate the validity of the approach in a dismantling use case, using a simulation with reduced quality as simulated system, and a simulation with full resolution where we add noise to the trajectories and some physical parameters as a representation of the real system.

While suction cups are widely used in Robotics, the literature is underdeveloped when it comes to the modelling and simulation of the suction phenomenon. In this paper, we present a novel physically-based approach to simulate the behavior of active suction cups. Our model relies on a novel formulation which assumes the pressure exerted on a suction cup during active control is based on constraint resolution. Our algorithmic implementation uses a classification process to handle the contacts during the suction phenomenon of the suction cup on a surface. Then, we formulate a convenient way for coupling the pressure constraint with the multiple contact constraints. We propose an evaluation of our approach through a comparison with real data, showing the ability of our model to reproduce the behavior of suction cups. Our approach paves the way for improving the design as well as the control of robotic actuators based on suction cups such as vaccum grippers.

In this paper, we propose 3D user interfaces (3DUI) that are adapted to specific Virtual Reality (VR) tasks: climbing a ladder using a puppet metaphor, piloting a drone thanks to a 3D virtual compass and stacking 3D objects with physics-based manipulation and time control. These metaphors have been designed to provide the user with an intuitive, playful and efficient way to perform each task.

Human-like characters can be modeled by suitable skeletal structures, which basically consist in trees where edges represent bones and vertices are joints between two adjacent bones.Motion is then defined as variations of the joints' configuration (i.e., partial rotations) over time, which also influences joint positions.However, this representation does not allow to easily represent the relationship between joints that are not directly connected by a bone.This work is therefore based on the premise that variations of the relative distances between such joints are important to represent complex human motions.While the former representations are currently used in practice for playing and analyzing motions, the latter can help in modeling a new class of problems where the relationships in human motions need to be simulated.Our main interest in this work is in adapting previously captured human postures (one frame of a given motion) with the aim of satisfying a certain number of geometrical constraints, which turn out to be easily definable in terms of distances.We present a novel procedure for approximating the relative inter-joint distances for skeletal structures having arbitrary features and respecting a predefined posture.This set of inter-joint distances defines an instance of the Distance Geometry Problem (DGP), that we tackle with a non-monotone spectral gradient method.

In character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphology-independent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.