Human Stiffness Perception and Learning in Interacting With Compliant Environments.

Humans are capable of adjusting their posture stably when interacting with a compliant surface. Their whole-body motion can be modulated in order to respond to the environment and reach to a stable state. In perceiving an uncertain external force, humans repetitively push it and learn how to produce a stable state. Research in human motor control has led to the hypothesis that the central nervous system integrates an internal model with sensory feedback in order to generate accurate movements. However, how the brain understands external force through exploration movements, and how humans accurately estimate a force from their experience of the force, is yet to be fully understood. To address these questions, we tested human behaviour in different stiffness profiles even though the force at the goal was the same. We generated one linear and two non-linear stiffness profiles, which required the same force at the target but different forces half-way to the target; we then measured the differences in the learning performance at the target and the differences in perception at the half-way point. Human subjects learned the stiffness profile through repetitive movements in reaching the target, and then indicated their estimation of half of the target value (position and force separately). This experimental design enabled us to probe how perception of the force experienced in different profiles affects the participants' estimations. We observed that the early parts of the learning curves were different for the three stiffness profiles. Secondly, the position estimates were accurate independent of the stiffness profile. The estimation in position was most likely influenced by the external environment rather than the profile itself. Interestingly, although visual information about the target had a large influence, we observed significant differences in accuracy of force estimation according to the stiffness profile.

Exploiting Spherical Projections To Generate Human-Like Wrist Pointing Movements.

The mechanism behind the generation of human movements is of great interest in many fields (e.g. robotics and neuroscience) to improve therapies and technologies. Optimal Feedback Control (OFC) and Passive Motion Paradigm (PMP) are currently two leading theories capable of effectively producing human-like motions, but they require solving nonlinear inverse problems to find a solution. The main benefit of using PMP is the possibility of generating path-independent movements consistent with the stereotypical behaviour observed in humans, while the equivalent OFC formulation is path-dependent. Our results demonstrate how the path-independent behaviour observed for the wrist pointing task can be explained by spherical projections of the planar tasks. The combination of the projections with the fractal impedance controller eliminates the nonlinear inverse problem, which reduces the computational cost compared to previous methodologies. The motion exploits a recently proposed PMP architecture that replaces the nonlinear inverse optimisation with a nonlinear anisotropic stiffness impedance profile generated by the Fractal Impedance Controller, reducing the computational cost and not requiring a task-dependent optimisation.

HapFIC: An Adaptive Force/Position Controller for Safe Environment Interaction in Articulated Systems.

Haptic interaction is essential for the dynamic dexterity of animals, which seamlessly switch from an impedance to an admittance behaviour using the force feedback from their proprioception. However, this ability is extremely challenging to reproduce in robots, especially when dealing with complex interaction dynamics, distributed contacts, and contact switching. Current model-based controllers require accurate interaction modelling to account for contacts and stabilise the interaction. In this manuscript, we propose an adaptive force/position controller that exploits the fractal impedance controller's passivity and non-linearity to execute a finite search algorithm using the force feedback signal from the sensor at the end-effector. The method is computationally inexpensive, opening the possibility to deal with distributed contacts in the future. We evaluated the architecture in physics simulation and showed that the controller can robustly control the interaction with objects of different dynamics without violating the maximum allowable target forces or causing numerical instability even for very rigid objects. The proposed controller can also autonomously deal with contact switching and may find application in multiple fields such as legged locomotion, rehabilitation and assistive robotics.

An Optimization-Based Locomotion Controller for Quadruped Robots Leveraging Cartesian Impedance Control.

Quadruped robots require compliance to handle unexpected external forces, such as impulsive contact forces from rough terrain, or from physical human-robot interaction. This paper presents a locomotion controller using Cartesian impedance control to coordinate tracking performance and desired compliance, along with Quadratic Programming (QP) to satisfy friction cone constraints, unilateral constraints, and torque limits. First, we resort to projected inverse-dynamics to derive an analytical control law of Cartesian impedance control for constrained and underactuated systems (typically a quadruped robot). Second, we formulate a QP to compute the optimal torques that are as close as possible to the desired values resulting from Cartesian impedance control while satisfying all of the physical constraints. When the desired motion torques lead to violation of physical constraints, the QP will result in a trade-off solution that sacrifices motion performance to ensure physical constraints. The proposed algorithm gives us more insight into the system that benefits from an analytical derivation and more efficient computation compared to hierarchical QP (HQP) controllers that typically require a solution of three QPs or more. Experiments applied on the ANYmal robot with various challenging terrains show the efficiency and performance of our controller.

Analytic Model for Quadruped Locomotion Task-Space Planning.

Despite the extensive presence of the legged locomotion in animals, it is extremely challenging to be reproduced with robots. Legged locomotion is an dynamic task which benefits from a planning that takes advantage of the gravitational pull on the system. However, the computational cost of such optimization rapidly increases with the complexity of kinematic structures, rendering impossible real-time deployment in unstructured environments. This paper proposes a simplified method that can generate desired centre of mass and feet trajectory for quadrupeds. The model describes a quadruped as two bipeds connected via their centres of mass, and it is based on the extension of an algebraic bipedal model that uses the topology of the gravitational attractor to describe bipedal locomotion strategies. The results show that the model generates trajectories that agrees with previous studies. The model will be deployed in the future as seed solution for whole-body trajectory optimization in the attempt to reduce the computational cost and obtain real-time planning of complex action in challenging environments.

Co-ordination of the upper and lower limbs for vestibular control of balance.

KEY POINTS: When standing and holding an earth-fixed object, galvanic vestibular stimulation (GVS) can evoke upper limb responses to maintain balance. In the present study, we determined how these responses are affected by grip context (no contact, light grip and firm grip), as well as how they are co-ordinated with the lower limbs to maintain balance. When GVS was applied during firm grip, hand and ground reaction forces were generated. The directions of these force vectors were co-ordinated such that the overall body sway response was always aligned with the inter-aural axis (i.e. craniocentric). When GVS was applied during light grip (< 1 N), hand forces were secondary to body movement, suggesting that the arm performed a mostly passive role. These results demonstrate that a minimum level of grip is required before the upper limb becomes active in balance control and also that the upper and lower limbs co-ordinate for an appropriate whole-body sway response. ABSTRACT: Vestibular stimulation can evoke responses in the arm when it is used for balance. In the present study, we determined how these responses are affected by grip context, as well as how they are co-ordinated with the rest of the body. Galvanic vestibular stimulation (GVS) was used to evoke balance responses under three conditions of manual contact with an earth-fixed object: no contact, light grip (< 1 N) (LG) and firm grip (FG). As grip progressed along this continuum, we observed an increase in GVS-evoked hand force, with a simultaneous reduction in ground reaction force (GRF) through the feet. During LG, hand force was secondary to the GVS-evoked body sway response, indicating that the arm performed a mostly passive role. By contrast, during FG, the arm became actively involved in driving body sway, as revealed by an early force impulse in the opposite direction to that seen in LG. We then examined how the direction of this active hand vector was co-ordinated with the lower limbs. Consistent with previous findings on sway anisotropy, FG skewed the direction of the GVS-evoked GRF vector towards the axis of baseline postural instability. However, this was effectively cancelled by the hand force vector, such that the whole-body sway response remained aligned with the inter-aural axis, maintaining the craniocentric principle. These results show that a minimum level of grip is necessary before the upper limb plays an active role in vestibular-evoked balance responses. Furthermore, they demonstrate that upper and lower-limb forces are co-ordinated to produce an appropriate whole-body sway response.

Optimal control of reaching includes kinematic constraints.

We investigate adaptation under a reaching task with an acceleration-based force field perturbation designed to alter the nominal straight hand trajectory in a potentially benign manner: pushing the hand off course in one direction before subsequently restoring towards the target. In this particular task, an explicit strategy to reduce motor effort requires a distinct deviation from the nominal rectilinear hand trajectory. Rather, our results display a clear directional preference during learning, as subjects adapted perturbed curved trajectories towards their initial baselines. We model this behavior using the framework of stochastic optimal control theory and an objective function that trades off the discordant requirements of 1) target accuracy, 2) motor effort, and 3) kinematic invariance. Our work addresses the underlying objective of a reaching movement, and we suggest that robustness, particularly against internal model uncertainly, is as essential to the reaching task as terminal accuracy and energy efficiency.