International Journal of Humanoid Robotics

This paper contributes toward the benchmarking of control architectures for bipedal robot locomotion. It considers architectures that are based on the Divergent Component of Motion (DCM) and composed of three main layers: trajectory optimization, simplified model control, and whole-body quadratic programming (QP) control layer. While the first two layers use simplified robot models, the whole-body QP control layer uses a complete robot model to produce either desired positions, velocities, or torques inputs at the joint-level. This paper then compares two implementations of the simplified model control layer, which are tested with position, velocity, and torque control modes for the whole-body QP control layer. In particular, both an instantaneous and a Receding Horizon controller are presented for the simplified model control layer. We show also that one of the proposed architectures allows the humanoid robot iCub to achieve a forward walking velocity of 0.3372m/s, which is the highest walking velocity achieved by the iCub robot.

In this paper, we propose a locomotion planning framework for a humanoid robot with stable whole-body collision avoidance motion, which enables the robot to traverse an unknown narrow space on the spot based on environmental measurements. The key idea of the proposed method is to reduce a large computational cost for the whole-body locomotion planning by utilizing global footstep planning results and its centroidal trajectory as a guide. In the global footstep planning phase, we modify the bounding box of the robot approximating the centroidal sway amplitude of the candidate footsteps. This enables the planner to obtain appropriate footsteps and transition time for next whole-body motion planning. Then, we execute sequential whole-body motion planning by prioritized inverse kinematics considering collision avoidance and maintaining its ZMP trajectory, which enables the robot to plan stable motion for each step in 223ms at worst. We evaluated the proposed framework by a humanoid robot HRP-5P in the dynamic simulation and the real world. The major contribution of our paper is solving the problem of increasing computational cost for whole-body motion planning and enabling a humanoid robot to execute adaptive on-site locomotion planning in an unknown narrow space.

This work introduces a new sensing system for biped robots based on plantar robot skin, which provides not only the resultant forces applied on the ankles but a precise shape of the pressure distribution in the sole together with other extra sensing modalities (temperature, pre-touch and acceleration). The information provided by the plantar robot skin can be used to compute the center of pressure and the ground reaction forces. This information also enables the online construction of the supporting polygon and its preemptive shape before foot landing using the proximity sensors in the robot skin. Two experiments were designed to show the advantages of this new sensing technology for improving balance and walking controllers for biped robots over unknown terrain.

Most existing motion control methods for humanoids aim at avoiding falling. However, the humanoid is generally an unstable system that cannot completely avoid falling and it is difficult to cope with the sudden fall of a robot. This paper designs a planning method of fall protection for humanoids according to the human falling motion. This method changes the impact position between the robot and ground by adjusting the motion of the robot as it falls. To further reduce damage to the robot, an appropriate cushioning material is installed at the point of impact to buffer the robot. The effectiveness of the proposed method is verified for a BHR6P humanoid robot falling in simulations and experiments.

This paper proposes a new framework to generate 3D multi-contact locomotion with low computation cost. The proposed framework consists of (a) the derivation of the prospect centroidal dynamics by introducing a force distribution ratio, where it can be represented with a formulation similar to the inverted pendulum’s one, and (b) the development of a fast computation method for generating a 3D center-of-mass (CoM) trajectory. Then (c) the ZMP reference is modified so that feasible contact wrench can be generated by a force distribution using the centroidal dynamics with the approximated friction cone. The proposed method allows to generate a trajectory sequentially and to change the locomotion parameters at any time even under variable CoM height. Then, the contact timing of each end-effector can be adjusted to synchronize with the actual contact with the environment by shortening or extending the desired duration of the support phase. This can be used to improve the robustness of the locomotion. The validity of the proposed method is confirmed by several numerical results in dynamic simulator: a CoM motion while changing the contact timing, a multi-contact locomotion considering a transition between biped and quadruped walking on an horizontal floor to move below obstacles. Finally, we also show a climbing stairs using handrail which requires dynamic changes of unilateral and bilateral contacts.

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

There has been great progress in soft robot design, manufacture, and control in recent years, and soft robots are a tool of choice for safe and robust handling of objects in conditions of uncertainty. Still, dexterous in-hand manipulation using soft robots remains a challenge. This paper introduces foam robot hands actuated by tendons sewn through a fabric glove. The flexibility of tendon actuation allows for high competence in utilizing deformation for robust in-hand manipulation. We discuss manufacturing, control, and design optimization for foam robots and demonstrate robust grasping and in-hand manipulation on a variety of different physical hand prototypes.

Robot performance measures are important tools for quantifying the ability to carry out manipulation tasks. Generally, these measures examine the system’s kinematic transformations from configuration to task space. This means that environmental constraints are neglected in spite of the significant effects they may have on the robot’s admissible motions. In this paper, we propose a new measure called the constrained manipulability polytope (CMP) that considers the system’s kinematic structure, including closed chains or composite sub-mechanisms, joint limits and the presence of obstacles. For an illustrative planar case, we demonstrate how the CMP can evaluate a robot’s performance in a cluttered scene and how this evaluation can be extrapolated to obtain a workspace visualization. Additionally, we show the advantages and limitations of the CMP compared to the state of the art. Furthermore, the method is demonstrated both in simulation and experimentally for NASA’s Valkyrie robot. We show how the CMP provides a measure for single-arm and dual-arm manipulation tasks, analyze the workspace and be used to optimize the robot’s posture.

Intelligent assistive robots can potentially contribute to maintaining an elderly person’s independence by supporting everyday life activities. However, the number of different and personalized activities to be supported renders pre-programming of all respective robot behaviors prohibitively difficult. Instead, to cope with a continuous and potentially open-ended stream of cooperative tasks, new collaborative robot behaviors need to be continuously learned and updated from demonstrations. To this end, we introduce an online learning method to incrementally build a cooperative skill library of probabilistic interaction primitives. The resulting model chooses a corresponding robot response to a human movement where the human intention is extracted from previously demonstrated movements. While existing batch learning methods for movement primitives usually learn such skill libraries only once for a pre-defined number of different skills, our approach enables extending the skill library in an open-ended and online fashion from new incoming demonstrations. The proposed approach is evaluated on a low-dimensional benchmark task and in a collaborative scenario with a 7DoF robot, where we also investigate the generalization of learned skills between different subjects.

Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains.