Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain

In this paper we tackle the problem of visually predicting surface friction for environments with diverse surfaces, and integrating this knowledge into biped robot locomotion planning. The problem is essential for autonomous robot locomotion since diverse surfaces with varying friction abound in the real world, from wood to ceramic tiles, grass or ice, which may cause difficulties or huge energy costs for robot locomotion if not considered. We propose to estimate friction and its uncertainty from visual estimation of material classes using convolutional neural networks, together with probability distribution functions of friction associated with each material. We then robustly integrate the friction predictions into a hierarchical (footstep and full-body) planning method using chance constraints, and optimize the same trajectory costs at both levels of the planning method for consistency. Our solution achieves fully autonomous perception and locomotion on slippery terrain, which considers not only friction and its uncertainty, but also collision, stability and trajectory cost. We show promising friction prediction results in real pictures of outdoor scenarios, and planning experiments on a real robot facing surfaces with different friction

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

Planification de pas pour robots humanoïdes : approches discrètes et continues

Dans cette thèse nous nous intéressons à deux types d'approches pour la planification de pas pour robots humanoïdes : d'une part les approches discrètes où le robot n'a qu'un nombre fini de pas possibles, et d'autre part les approches où le robot se base sur des zones de faisabilité continues. Nous étudions ces problèmes à la fois du point de vue théorique et pratique. En particulier nous décrivons deux méthodes originales, cohérentes et efficaces pour la planification de pas, l'une dans le cas discret (chapitre 5) et l'autre dans le cas continu (chapitre 6). Nous validons ces méthodes en simulation ainsi qu'avec plusieurs expériences sur le robot HRP-2. ABSTRACT : In this thesis we investigate two types of approaches for footstep planning for humanoid robots: on one hand the discrete approaches where the robot has only a finite set of possible steps, and on the other hand the approaches where the robot uses continuous feasibility regions. We study these problems both on a theoretical and practical level. In particular, we describe two original, coherent and efficient methods for footstep planning, one in the discrete case (chapter 5), and one in the continuous case (chapter 6). We validate these methods in simulation and with several experiments on the robot HRP-2

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Exploiting structure in humanoid motion planning

If humanoid robots should work along with humans and should be able to solve repetitive tasks, we need to enable them with a skill to autonomously plan motions. Motion planning is a longstanding core problem in robotics, and while its algorithmic foundation has been studied in depth, motion planning is still an NP-hard problem lacking efficient solutions. We want to open up a new perspective on the problem by highlighting its structure: the behavior of the robot, the mechanical system of the robot, and the environment of the robot. We will investigate the hypothesis that each structural component can be exploited to create more efficient motion planning algorithms. We present three algorithms exploiting structure, based on geometrical and topological arguments: first, we exploit the behavior of a walking robot by studying the feasibility of footstep transitions. The resulting algorithm is able to plan footsteps avoiding up to 60 objects on a 6 square meters planar surface. Second, we exploit the mechanical system of a humanoid robot by studying the linear linkage structures of its arms and legs. We introduce the concept of an irreducible motion, which is a completeness-preserving dimensionality reduction technique. The resulting algorithm is able to find motions in narrow environments, where previous sampling-based methods could not be applied. Third, we exploit the environment by reasoning about the topological structure of contact transitions. We show that analyzing the environment is an efficient method to precompute relevant information for efficient motion planning. Based on those results, we come to the conclusion that exploiting structure is an essential component of efficient motion planning. It follows that any humanoid robot, who wants to act efficiently in the real world, needs to be able to understand and to exploit structure

Humanoid manipulation and locomotion with real-time footstep optimization

Cette thèse porte sur la réalisation des tâches avec la locomotion sur des robots humanoïdes. Grâce à leurs nombreux degrés de liberté, ces robots possèdent un très haut niveau de redondance. D’autre part, les humanoïdes sont sous-actionnés dans le sens où la position et l’orientation ne sont pas directement contrôlées par un moteur. Ces deux aspects, le plus souvent étudiés séparément dans la littérature, sont envisagés ici dans un même cadre. En outre, la génération d’un mouvement complexe impliquant à la fois des tâches de manipulation et de locomotion, étudiée habituellement sous l’angle de la planification de mouvement, est abordée ici dans sa composante réactivité temps réel. En divisant le processus d’optimisation en deux étapes, un contrôleur basé sur la notion de pile de tâches permet l’adaptation temps réel des empreintes de pas planifiées dans la première étape. Un module de perception est également conçu pour créer une boucle fermée de perception-décision-action. Cette architecture combinant planification et réactivité est validée sur le robot HRP-2. Deux classes d’expériences sont menées. Dans un cas, le robot doit saisir un objet éloigné, posé sur une table ou sur le sol. Dans l’autre, le robot doit franchir un obstacle. Dans les deux cas, les condition d’exécution sont mises à jour en temps réel pour faire face à la dynamique de l’environnement : changement de position de l’objet à saisir ou de l’obstacle à franchir. ABSTRACT : This thesis focuses on realization of tasks with locomotion on humanoid robots. Thanks to their numerous degrees of freedom, humanoid robots possess a very high level of redundancy. On the other hand, humanoids are underactuated in the sense that the position and orientation of the base are not directly controlled by any motor. These two aspects, usually studied separately in manipulation and locomotion research, are unified in a same framework in this thesis and are resolved as one unique problem. Moreover, the generation of a complex movement involving both tasks and footsteps is also improved becomes reactive. By dividing the optimization process into appropriate stages and by feeding directly the intermediate result to a task-based controller, footsteps can be calculated and adapted in real-time to deal with changes in the environment. A perception module is also developed to build a closed perception-decision-action loop. This architecture combining motion planning and reactivity validated on the HRP-2 robot. Two classes of experiments are carried out. In one case the robot has to grasp an object far away at different height level. In the other, the robot has to step over an object on the floor. In both cases, the execution conditions are updated in real-time to deal with the dynamics of the environment: changes in position of the target to be caught or of the obstacle to be stepped over

Reachability Map for Diverse and Energy Efficient Stepping of Humanoids

In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the totality of robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a two-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality, resulting in optimal step positions with a 10.9% lower energy cost than those generated by linear inverted pendulum model. Despite the complexity of this nonlinear process, we found that near-minimal effort stepping locations are within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the nonuniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans

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