Joris Vaillant

Generation of whole-body optimal dynamic multi-contact motions

We propose a method to plan optimal whole-body dynamic motion in multi-contact non-gaited transitions. Using a B-spline time parameterization for the active joints, we turn the motion-planning problem into a semi-infinite programming formulation that is solved by nonlinear optimization techniques. Our main contribution lies in producing constraint-satisfaction guaranteed motions for any time grid. Indeed, we use Taylor series expansion to approximate the dynamic and kinematic models over fixed successive time intervals, and transform the problem (constraints and cost functions) into time polynomials which coefficients are function of the optimization variables. The evaluation of the constraints turns then into computation of extrema (over each time interval) that are given to the solver. We also account for collisions and self-collisions constraints that have not a closed-form expression over the time. We address the problem of the balance within the optimization problem and demonstrate that generating whole-body multi-contact dynamic motion for complex tasks is possible and can be tractable, although still time consuming. We discuss thoroughly the planning of a sitting motion with the HRP-2 humanoid robot and assess our method with several other complex scenarios.

Model Preview Control in Multi-Contact Motion- Application to a Humanoid Robot

Our work builds largely on Nagasakas stabilizer in multi-contact motion [1]. Using a sequence of contact stances from an offline multi-contact planner, we use first a Model Predictive Controller to generate a dynamic trajectory of the center of mass, then a whole-body closed-loop model-based controller to track it at best. Relatively to Nagasakas work, we allow frame changes of the preferred force, provide a heuristic to compute the timing of the transition from purely geometrical features and investigate the synchronization problem between the reduced-model preview control and the whole-body controller. Using our framework, we generate a wide range of 3D motions, while accounting for predictable external forces, which includes transporting objects. Simulation scenarios are presented and obtained results are analyzed and discussed.

Exploring humanoid robots locomotion capabilities in virtual disaster response scenarios

We study the feasibility of having various humanoid robots undertake some tasks from those challenged by the DARPAs call on disaster operations. Hence, we focus on locomotion tasks that apparently require human-like motor skills to be achieved. We use virtual scenes under the fully-3D-modeled-environment assumption. The robot autonomously plans and executes the motion with a high-level goal specification, such as reaching a global position or a particular contact state. We assess the feasibility according not only to the robot kinematics, but also to whole-body dynamics, non-desired collision avoidance, friction limits, and actuation limits. The results -the controlled motions- are demonstrated in the accompanying video.

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.

Vertical ladder climbing by the HRP-2 humanoid robot

We report the results obtained from our trials in making the HRP-2 humanoid robot climb vertical industrial-norm ladders. We integrated our multi-contact planner and multi-objective QP control as basic components. First, a set of contacts to climb the ladder is planned off-line and provided as an input for a finite state machine that sequences tasks to be realized by our multi-objective model-based QP in closed-loop control. The trials we made revealed that hardware changes are to be made on the HRP-2, and the software has to be made more robust. Yet, we confirmed that HRP-2 has power capability to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturings (e.g. airliners, shipyards and buildings).

Brain-machine interfacing control of whole-body humanoid motion.

We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

Compliant control of a humanoid robot helping a person stand up from a seated position

Supporting somebody while standing up from a seated position is one of the most frequent tasks envisaged for a humanoid assistant used in order to help somebody with reduced mobility around the house. The safest way to do this for a human carer has been clearly defined in patient handling guides for hospitals where a policy of never lifting the patient is strongly recommended. Based on these guidelines, this paper proposes a stable initial posture for a humanoid robot supporting a person from sitting to standing, as well as a control law to make the robot keep a contact force and follow the motion of the person compliantly. This initial position and compliant control of the robot was implemented and tested on the humanoid HRP-4 interacting with a healthy subject.

Multi-Character Physical and Behavioral Interactions Controller

We extend the quadratic program (QP)-based task-space character control approach—initially intended for individual character animation—to multiple characters interacting among each other or with mobile/articulated elements of the environment. The interactions between the characters can be either physical interactions, such as contacts that can be established or broken at will between them and for which the forces are subjected to Newton’s third law, or behavioral interactions, such as collision avoidance and cooperation that naturally emerge to achieve collaborative tasks from high-level specifications. We take a systematic approach integrating all the equations of motions of the characters, objects, and articulated environment parts in a single QP formulation in order to embrace and solve the most general instance of the problem, where independent individual character controllers would fail to account for the inherent coupling of their respective motions through those physical and behavioral interactions. Various types of motions/behaviors are controlled with only the one single formulation that we propose, and some examples of the original motions the framework allows are presented in the accompanying video.

Point-cloud multi-contact planning for humanoids: Preliminary results

We present preliminary results in porting our multi-contact non-gaited motion planning framework to operate in real environments where the surroundings are acquired using an embedded camera together with a depth map sensor. We consider the robot to have no a priori knowledge of the environment, and propose a scheme to extract the information relevant for planning from an acquired point cloud. This yield the basis of an egocentric on-the-fly multi-contact planner. We then demonstrate its capacity with two simulation scenarios involving an HRP-2 robot in various environment before discussing some issues to be addressed in our quest to achieve a close loop between planning and execution in an environment explored through embedded sensors.

Integration of non-inclusive contacts in posture generation

In this paper we propose a simple way to formulate geometric contact formation to have an arbitrary intersection shape in a robotic (humanoid) posture generation problem. The contact shape is the outcome of our posture generator that is formulated as a non-linear optimization programming to fulfill a large variety of robot intrinsic limitations (e.g. joint and torque limits) and tasks (e.g. desired contact). Starting by defining convex areas of contact on the robots body and the environment, that we call contact patches, we can generate contacts with arbitrary intersection of a pair of any of these predefined patches. Our geometric contact modeling writes very simply as additional constraints and variables added to the optimization problem, translating the search for an ellipse inscribed in the intersection of the pair of patches we want in contact. The result of our posture generator is then a configuration where contact patches are not necessarily included in one another. This allows our posture generator to propose contacts of different shapes with a non-predefined number of contact points (used later to compute reaction/contact forces). We illustrate the efficiency of our method in multi-contact posture generation with the HRP-2 and ATLAS humanoid robots with results that can not be generated automatically by existing methods.