Masaki Murooka

Generating whole-body motion keep away from joint torque, contact force, contact moment limitations enabling steep climbing with a real humanoid robot

For humanoid robots to perform whole-body motions, a motion planner should generate feasible motions satisfying various constraints including joint torque limitation, friction, balancing, collision, and so on. Furthermore, for life-size humanoid robots to perform higher-load motions, such as climbing ladders, safely, it is important to generate motions which requirements are not too close to the limitations. In this paper, we propose a humanoid motion planner based on Body Retention Load Vector (BRLV), which is a novel index for representing severity of physical constraints: limitation of joint Torque, contact Force, and contact Moment (TFM limitations). By minimizing the norm of BRLV, we obtain humanoid motions that are farthest from TFM limitations. Finally, we evaluate the proposed motion planner in simulation and confirm the effectiveness of the planner through experiments in which a life-size humanoid robot climbs a ladder and a car.

Manipulation strategy decision and execution based on strategy proving operation for carrying large and heavy objects

In case that a robot carries large and heavy objects with unknown physical parameters such as mass automatically, the autonomous decision and execution of the manipulation strategy are necessary. The method to decide the proper strategy from the various candidates depending on the object is a difficult problem and not researched widely. We consider the operation as the mapping from the physical parameter space to the object motion space. Based on the concept of mapping, we define the strategy proving operation (SPO) for determination of strategy feasibility. We introduce two examples of SPO and construct the system for deciding strategy from lifting, pushing, and pivoting. Executing the strategy in the situation that physical parameters are not known is also necessary. We construct the generator and controller for the full-body manipulation, which can be employed regardless of strategy. The controller enables the robot to exert adequate force while keeping balance. We clarify the applicable scope of the proposed method and show that a life-sized humanoid decides the strategy and carries various large and heavy objects autonomously through the experiment.

Whole-body pushing manipulation with contact posture planning of large and heavy object for humanoid robot

Humanoid robot is able to execute various behavior to manipulate objects because of high degree-of-freedom around the whole-body. Although hands contact with objects and exert force in ordinary pushing motion by robot, pushing motion contacting with the object at various regions of whole-body has potential for extending the scope of feasible manipulation. We derive the fundamental formulas of humanoid robot in the situation that the external force is applied to the arbitrary region of whole-body, and then propose the method to generate and execute the pushing motion based on the formulas. The proposed method is generalized for enabling to select a contact point with an object from whole-body regions and control the pushing force applied to the sensorless region. In order to verify the effectiveness, we show the experimental result that a lifesized humanoid carries large and heavy objects by pushing with various regions of whole-body.

Development of humanoid robot system for disaster response through team NEDO-JSK's approach to DARPA Robotics Challenge Finals

This paper presents Team NEDO-JSKs approach to the development of novel humanoid platform for disaster response through participation to DARPA Robotics Challenge Finals. This development is a part of the project organized by New Energy and Industrial Technology Development Organization. Technology for this robot is based on the recent research of high-speed and high-torque motor driver with water-cooling system, RTM-ROS inter-operation for intelligent robotics, and generation of full-body fast dancing motion, due to the generic 10 years research of HRP-2 as a platform humanoid robot. Development target is the robot support in a variety of unsafe human tasks teleoperated by humans in case of a disaster response, equipped with body structure capability for use of human devices and tools in human environment, performance for dynamic full-body actions covering human-sized speed and power, and basic function for intelligent and integrated robot platform system for performing various tasks independently. we also describes NEDO-JSK teams approach to design methodology for robot hardware and architecture of software system and user interface for DRC Finals as a test case of disaster response.

Multi-layered real-time controllers for humanoid's manipulation and locomotion tasks with emergency stop

This paper describes a practical method to construct real-time controllers to achieve locomotion and manipulation tasks with a humanoid robot. We propose a method to insert emergency stop functionality to each layer to avoid robots falling down and joint overloads even if recognition and planning error exist. We explain implementation of multi-layered real-time controllers on HRP2 robot and application to several manipulation and locomotion tasks. Finally, we evaluate emergency stop functionality in several manipulation tasks.

Configurable autonomy applicable to humanoid manipulation in unstructured and communication-limited environment

In recent years, humanoids have been expected to play an important part in disaster response due to safety concerns. For disaster response, humanoids should do tasks in unknown and unstructured environments possibly with limited communications. Firstly this paper presents a robot operating system in which complementary integration of autonomous and manual functions is achieved. In our system operator can change the level of automation depending on the situation: operator can modify the result of recognition in 3D Viewer, and can transfer from auto motion generating mode to manual control mode at any time with inheriting some motion parameters. Secondly for the purpose of overcoming communication-limit in disaster site, we propose the method of generating robot motion with little communication between operator and robot. Even when communication is limited, our System can convey necessary information to user by processing past data, always transferring small important data, and showing future motion plans.

Online estimation of object-environment constraints for planning of humanoid motion on a movable object

This paper shows a method for achieving multi-contact motion for a humanoid robot on a movable object, such as climbing of a stepladder. Recent research has developed methods for achieving multi-contact motion that considers various constraints, such as joint limits, torques, balance constraints, reachability, and collision avoidance. In addition to these constraints, Motion On a Movable Object (MOMO) has the following features: it has to consider an objects balance during the changing of contact points; and it has to handle scenarios where the mass properties of an object are unknown. In this paper, in order to achieve a humanoid robot having MOMO, we propose balance constraints that consider the constraints imposed by an object as well as an online estimation of objects constraints. First, we use object-environment constraints as the robots constraints, and then we show a method for estimating them based on information provided by the robots sensors. Next, we show a method for applying the balance constraints to a humanoid motion planner and for executing planned motion with real-time sensor feedback controller. Finally, we evaluate our proposed method through experiments in which a life-sized humanoid robot climbs stepladders that have unknown mass properties.

Whole-body holding manipulation by humanoid robot based on transition graph of object motion and contact

Whole-body holding manipulation is effective for carrying the handleless large object. In order to keep the object stability, the dexterous transition motion is necessary. From geometric and physical conditions of object manipulation, we propose the general method of generating the transition graph, which represents the object pose and grasp contact. By searching the path on the graph, the transition motion is planned automatically with considering the object motion and contact switching simultaneously. By generating and modifying the whole-body holding motion, the planned object motion is achieved stably. We show the effectiveness of the proposed method by the experiments, in which robot lifts up a large object with whole-body contact by the planned transition motion.

Global planning of whole-body manipulation by humanoid robot based on transition graph of object motion and contact switching

Abstract Humanoid robot has the potential to achieve various manipulation strategies such as lifting, pushing, pivoting and rolling. Deciding which strategies to apply for the object manipulation is difficult because the appropriate manipulation motion varies greatly depending on the object property. We propose the planning method of whole-body manipulation for various manipulati on strategies. In order to plan the manipulation in the global scope, we generate the graph representing all the possible object poses and operation contacts. By searching the feasible path in the graph, we plan the sequence of object states satisfying the kinematics and dynamics conditions. Finally, the robot postures for the whole-body manipulation are generated from the object states. The proposed method is efficient because the tractable object configuration is first planned in the global scope and then the complicated robot configuration is calculated. We show the effectiveness of the proposed method by planning the humanoid robot motion for various manipulation strategies automatically from the object and robot models. Graphical Abstract

Planning and execution of groping behavior for contact sensor based manipulation in an unknown environment

Groping behavior based on contact sensors is necessary for manipulation in an unknown environment. For those situations, it is effective for a robot to accumulate contact information as an environment map, and to plan the motions for executing the safe trial motion. We first propose a method of updating the occupancy grid map of the manipulation region from the contact information by introducing the contact sensor model. Using this map, we propose a method of sampling-based motion planning that enables the execution of the safe trial motion based on the criteria of feasibility and safety. To verify the effectiveness, we show the experimentally obtained results, showing that a real robot plans and executes the manipulation with groping behavior in the occluded environment.