Stéphane Caron

Leveraging Cone Double Description for Multi-contact Stability of Humanoids with Applications to Statics and Dynamics

We build on previous works advocating the use of the Gravito-Inertial Wrench Cone (GIWC) as a general contact stability criterion (a “ZMP for non-coplanar contacts”). We show how to compute this wrench cone from the friction cones of contact forces by using an intermediate representation, the surface contact wrench cone, which is the minimal representation of contact stability for each surface contact. The observation that the GIWC needs to be computed only once per stance leads to particularly efficient algorithms, as we illustrate in two important problems for humanoids: “testing robust static equilibrium” and “time-optimal path parameterization”. We show, through theoretical analysis and in physics simulations, that our method is more general and/or outperforms existing ones.

Stability of surface contacts for humanoid robots: Closed-form formulae of the Contact Wrench Cone for rectangular support areas

Humanoids locomote by making and breaking contacts with their environment. Thus, a crucial question for them is to anticipate whether a contact will hold or break under effort. For rigid surface contacts, existing methods usually consider several point-contact forces, which has some drawbacks due to the underlying redundancy. We derive a criterion, the Contact Wrench Cone (CWC), which is equivalent to any number of applied forces on the contact surface, and for which we provide a closed-form formula. It turns out that the CWC can be decomposed into three conditions: (i) Coulomb friction on the resultant force, (ii) CoP inside the support area, and (iii) upper and lower bounds on the yaw torque. While the first two are well-known, the third one is novel. It can, for instance, be used to prevent the undesired foot yaws observed in biped locomotion. We show that our formula yields simpler and faster computations than existing approaches for humanoid motions in single support, and assess its validity in the OpenHRP simulator.

Kinodynamic Planning in the Configuration Space via Admissible Velocity Propagation

We propose a method that enables kinodynamic planning in the configuration space (of dimension n) instead of the state space (of dimension 2n), thereby potentially cutting down the complexity of usual kinodynamic planning algorithms by an exponential factor. At the heart of this method is a new technique – called Admissible Velocity Propagation (AVP) – which, given a path in the configuration space and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this technique with usual sampling-based methods gives rise to a family of new motion planners that can appropriately handle kinodynamic constraints while avoiding the complexity explosion and, to some extent, the conceptual difficulties associated with a move to the state space.

ZMP Support Areas for Multicontact Mobility Under Frictional Constraints

We propose a method for checking and enforcing multicontact stability based on the zero-tilting moment point (ZMP). The key to our development is the generalization of ZMP support areas to take into account: 1) frictional constraints and 2) multiple noncoplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots by using the linear pendulum mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call the pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multicontact motions where an HRP-4 humanoid locomotes in challenging multicontact scenarios.

Admissible velocity propagation: Beyond quasi-static path planning for high-dimensional robots

Path-velocity decomposition is an intuitive yet powerful approach to addressing the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler steps: first, a path is found in the configuration space that satisfies the geometric constraints (path planning), and second, a time-parameterization of that path satisfying the kinodynamic constraints is found. A fundamental requirement is that the path found in the first step must be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints during the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm – Admissible Velocity Propagation – which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path, while respecting the system’s kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail.

Completeness of randomized kinodynamic planners with state-based steering

Abstract Probabilistic completeness is an important property in motion planning. Although it has been established with clear assumptions for geometric planners, the panorama of completeness results for kinodynamic planners is still incomplete, as most existing proofs rely on strong assumptions that are difficult, if not impossible, to verify on practical systems. In this paper, we focus on an important class of kinodynamic planners, namely those that interpolate trajectories in the state space. We provide a proof of probabilistic completeness for such planners under assumptions that can be readily verified from the system’s equations of motion and the user-defined interpolation function. Our proof relies crucially on a property of interpolated trajectories, termed second-order continuity (SOC), which we show is tightly related to the ability of a planner to benefit from denser sampling. We analyze the impact of this property in simulations on a low-torque pendulum. Our results show that a simple RRT using a second-order continuous interpolation swiftly finds solution, while it is impossible for the same planner using standard Bezier curves (which are not SOC) to find any solution. 1

Supervoxel Plane Segmentation and Multi-Contact Motion Generation for Humanoid Stair Climbing

Stair climbing is still a challenging task for humanoid robots, especially in unknown environments. In this paper, we address this problem from perception to execution. Our first contribution is a real-time plane-segment estimation method using Lidar data without prior models of the staircase. We then integrate this solution with humanoid motion planning. Our second contribution is a stair-climbing motion generator where estimated plane segments are used to compute footholds and stability polygons. We evaluate our method on various staircases. We also demonstrate the feasibility of the generated trajectories in a real-life experiment with the humanoid robot HRP-4.

Multi-contact Motion Planning and Control

The essence of humanoid robots is their ability to reproduce human skills in locomotion and manipulation. Early efforts in humanoid research were dedicated to bipedal walking, first on flat terrains and recently on uneven ones, while the manipulation capabilities inherit from the literature in bimanual and dexterous-hand manipulation. In practice, the two problems interact largely. Locomotion in cluttered spaces benefits from extra contacts between any part of the robot and the environment, such as when grippers grasp a handrail during stair climbing, while legs can conversely enhance manipulation capabilities, such as when arching the whole body to augment contact pressure at an end effector. The two problems share the same background: they are governed by non-smooth dynamics (friction and impacts at contacts) under viability constraints including dynamic stability. Consequently, they are now solved jointly. This chapter highlights the state-of-the-art techniques used for this purpose in multi-contact planning and control.