Adrien Escande

Fast resolution of hierarchized inverse kinematics with inequality constraints

Classically, the inverse kinematics is performed by computing the singular value decomposition of the matrix to invert. This enables a very simple writing of the algorithm. However, the computation cost is high, especially when applied to complex robots and complex sets of constraints (typically around 5ms for 50 degrees of freedom - DOF). In this paper, we propose a dedicated adaptation of quadratic programming that enables fast computations of the hierarchical inverse kinematics (around 0.1ms for 50 DOF). We then extend this algorithm to deal with unilateral constraints, obtaining sufficiently high performances for reactive control.

Planning support contact-points for humanoid robots and experiments on HRP-2

This paper deals with the motion planning of a polyarticulated robotic system for which support contacts are allowed to occur between any part of the body and any part of the environment. Starting with a description of the environment and of a target, it computes a sequence of postures that allow our system to reach its target. We describe a very generic architecture of this planner, highly modular, as well as a first implementation of it. We then present our results, both simulations and real experiments, for a simple grasping task using the HRP-2 humanoid robot

Potential field guide for humanoid multicontacts acyclic motion planning

We present a motion planning algorithm that computes rough trajectories used by a contact-points planner as a guide to grow its search graph. We adapt collision-free motion planning algorithms to plan a path within the guide space, a submanifold of the configuration space included in the free space in which the configurations are subject to static stability constraint. We first discuss the definition of the guide space. Then we detail the different techniques and ideas involved: relevant C-space sampling for humanoid robot, task-driven projection process, static stability test based on polyhedral convex cones theorys double description method. We finally present results from our implementation of the algorithm.

Fast C 1 proximity queries using support mapping of sphere-torus-patches bounding volumes

STP-BV is a bounding volume made of patches of spheres and toruses. These patches are assembled so that a convex polyhedral hull is bulged, in a tunable way, into a strictly convex form. Strict convexity ensures at least C1 property of the distance function -and hence, its gradient continuity. STP-BV were introduced in our previous work [1], but proximity distance queries were limited to pairs of STP-BV covered objects. In this work we present an alternative to achieve fast proximity distance queries between a STP-BV object and any other convex shape. This is simply made by proposing a support mapping for STP-BV to be used with GJK algorithm [2] and its EPA extension to penetration cases [3]. Implementation and experiments of the proposed method and its performance are demonstrated with potential applications to robotics and computer graphics.

Contact planning for acyclic motion with tasks constraints

This paper extends our previous work on contact points planning in two ways. First, by taking advantage of the possibilities offered by our initial posture generator, we include additional tasks that are not related to locomotion within the planning. The output motion will be generated so as to cope with these tasks. Second, we refine the potential function that guides the planner by introducing potential fields acting each on a single body. This helps escaping local minima of the original potential field and thus to deal with more challenging scenarios. We then test these novelties on difficult problems with success, and experiment the output of one of the planned scenario on a HRP-2 humanoid robot.

Towards autonomous object reconstruction for visual search by the humanoid robot HRP-2

This paper deals with the problem of object reconstruction for visual search by a humanoid robot. Three problems necessary to achieve the behavior autonomously are considered: full-body motion generation according to a camera pose, general object representation for visual recognition and pose estimation, and far-away visual detection of an object. First we deal with the problem of generating full body motion for a HRP-2 humanoid robot to achieve camera pose given by a Next Best View algorithm. We use an optimization based approach including self-collision avoidance. This is made possible by a body to body distance function having a continuous gradient. The second problem has received a lot of attention for several decades, and we present a solution based on 3D vision together with SIFTs descriptor, making use of the information available from the robot. It is shown in this paper that one of the major limitation of this model is the perception distance. Thus a new approach based on a generative object model is presented to cope with more difficult situations. It relies on a local representation which allows handling occlusion as well as large scale and pose variations.

Interactive dynamics and balance of a virtual character during manipulation tasks

This paper proposes a new framework of online hybrid control for virtual characters, which combines multi-objective control and motion capture techniques. At each time step, the motions of the operator are captured and sent to the character. These captured motions help the character understand what task the operator wants it to perform. Then, the character decides by itself how to execute proper actions while maintaining its balance, in a virtual environment which is different from the real world. Given a manipulation task, the desired virtual task wrench is computed thanks to a force control approach. The control system takes these task wrenches into consideration and solves a constrained quadratic problem for joint torques, which are then used to drive the virtual character. The whole control problem is solved online so that the virtual character can interact in real-time with the virtual environment as well as the operator. Examples using this control framework are presented as results from simulation.

A Strictly Convex Hull for Computing Proximity Distances With Continuous Gradients

We propose a new bounding volume that achieves a tunable strict convexity of a given convex hull. This geometric operator is named sphere-tori-patches bounding volume (STP-BV), which is the acronym for the bounding volume made of patches of spheres and tori. The strict convexity of STP-BV guarantees a unique pair of witness points and at least C1 continuity of the distance function resulting from a proximity query with another convex shape. Subsequently, the gradient of the distance function is continuous. This is useful for integrating distance as a constraint in robotic motion planners or controllers using smooth optimization techniques. For the sake of completeness, we compare performance in smooth and nonsmooth optimization with examples of growing complexity when involving distance queries between pairs of convex shapes.

Task-driven posture optimization for virtual characters

This paper presents a generic approach to find optimal postures, including contact positions, for manipulation tasks. It can be used in either the preparation for a task, or the evaluation of the feasibility of a task during planning stages. With such an approach, an animator can control a virtual character from a high level by just specifying a task, such as moving an object along a desired path to a desired position; the animator does not need to manually find suitable postures for the task. For each task, an optimization problem is solved, which considers not only geometric and kinematic constraints, but also force and moment constraints. The optimized postures allow the virtual character to apply manipulation forces as strongly as possible, and meanwhile to avoid foot slipping. Moreover, potential perturbation forces can be taken into account in the optimization to make postures more robust. The realism of our approach is demonstrated with different types of manipulation tasks.

Interactive Virtual Humans: A Two-Level Prioritized Control Framework With Wrench Bounds

This paper presents a new control framework for virtual humans (VHs) in a physics-based virtual environment. This framework combines multiobjective control with motion capture techniques. Each motion-tracking task is associated with a task wrench. Bounds are imposed on lower priority task wrenches to ensure the controller performance of higher priority tasks. An optimization problem is solved to compute optimal task wrenches that are based on wrench bounds. Finally, joint torques are computed using the optimal task wrenches. The novelty of our wrench-bound method is that it can handle inequality constraints on a higher priority task and maintain passivity as well. This control framework allows an operator to interact with the VH in real time, without the necessity of compromising the VHs balance. It also allows the VH to generate appropriate motions to handle interactions with the virtual environment, rather than to simply emulate captured motions. The effectiveness of our approach is demonstrated by a VH performing reaching and manipulation tasks.