The interaction between an exoskeleton and its human user is complex, and needs to conform to various requirements related to safety, comfort and adaptability. It is however impractical to test a large number of prototype variations against a large number of user variations, especially in the initial design and testing phases. Model based methods can help at this design stage by providing a virtual testbed. In this study, we develop a MATLAB-based toolbox that can simulate the interaction between a human model and a lower limb exoskeleton during the sit to stand motion. We present results for different scales of human users as well as variation in levels of exoskeleton assistance. Our results show that large reductions in human joint torques upto 57Nm are possible, while also transmitting large forces upto 300N to the human model. Additionally, by varying the human body sizes by 15% we found that the interaction forces also changed by as much as 29.2%. Therefore, careful consideration of the human user and its limitation should be made in the exoskeleton design and concept phases.

This study proposes a model that combines a realistically scaled neural network made up of pools of spiking neurons, with a musculoskeletal model of the human arm. We used evidence from literature to design topological pools of spinal neurons and their synaptic connections. The spiking output of the motor neuron pools were used as the command signals that generated motor unit forces, and drove joint motion. Feedback information from muscle spindles was relayed to the neural network via monosynaptic and disynaptic pathways. Participant-specific parameters of the combined neuromusculoskeletal (NMS) system were then identified from recorded experimental data. The identified NMS model was used to simulate the arm stretch reflex and the results were validated by comparison to an independent recorded dataset. The models and methodology proposed in this study show that large and complex neural systems can be identified in conjunction with the musculoskeletal systems that they control. This additional layer of detail in NMS models has important relevance to the research communities related to rehabilitation robotics and human movement analysis.

Concurrent advancements in mechanical design and motion planning algorithms allow state-of-the-art humanoid robots to exhibit complex and realistic behavior. In face of this added complexity and the need for humanlike behavior, research has begun to look toward studies in human neuroscience to better organize and guide humanoid robot motion. In this paper, we present one such method of generating anthropomorphic motion by building the “invariants” of human movements and applying them as kinematic tasks. Whole-body motion of 14 healthy participants was recorded during a walking and grasping task. The recorded data were statistically analyzed to extract invariants which best described the observed motion. These invariants were expressed as a set of rules that were used to synthesize the stereotypy in human motion. We propose an algorithm that reproduces the key parameters of motion, taking into account the knowledge from human movement and the limitations of the target anthropomorph. The results are then generalized such that we can generate motion for targets which were not originally recorded. The algorithmic output is applied in a task-based prioritized inverse kinematics solver to generate dynamically stable and realistic anthropomorphic motion. We illustrate our results on the humanoid HRP-2 by making it walk to and grasp objects at various positions. Our approach complements classical optimization or motion-planning-based methods and provides interesting perspectives toward the use of human movements for deducing effective cost functions in optimization techniques or heuristics for planning algorithms.

Yoyo playing may seem easy for a human, but it is a challenging problem for a humanoid robot. This paper presents an approach to generate yoyo motions for the humanoid robot, HRP-2, based on motion recorded from human yoyo playing, dynamical models and numerical optimal control techniques. We recorded vertical yoyo playing of 4 subjects measuring yoyo height and rotation angle as well as the corresponding hand motions. A detailed multi-phase yoyo model with impact collisions and control patterns of human yoyo playing were identified from these measurements. Based on this knowledge, reliable yoyo motions within the feasibility ranges of HRP-2 were generated using optimal control. The resulting motions have been implemented on the robot using open-loop and event-based quasi open-loop control strategies.

This paper deals with the problem of generating realistic human-like reaching movements from a small set of movement primitives. Two kinds of movement databases are used as reference. The first one is obtained numerically, by applying biological principles of motor control on the dynamic model of the robot arm. The second one is obtained by recording reaching movements of human subjects. From these databases, primitives are extracted and analyzed by using Principal Component Analysis. An original generalization method is then proposed for generating movements that did not belong to the initial database. We show that twenty primitives allow to produce new movements, having characteristics similar to that of humans. Experiments on the humanoid robot HRP-2 are presented to illustrate the result.

We present a novel method of guiding a humanoid robot, including stepping, by allowing a user to move its head. The motivation behind this approach comes from research in the field of human neuroscience. In human locomotion it has been found that the head plays a very important role in guiding and planning motion. We use this idea to generate humanoid whole-body motion derived purely as a result of moving the head joint. The input to move the head joint is provided by a user via a 6D mouse. The algorithm presented in this study judges when further head movement leads to instability, and then generates stepping motions to stabilize the robot. By providing the software with autonomy to decide when and where to step, the user is allowed to simply steer the robot head (via visual feedback) without worrying about stability. We illustrate our results by presenting experiments conducted in simulation, as well as on our robot, HRP2.