For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have limited generalizability or data efficiency. What is missing, is the inclusion of human motor control models that can provide generalizable human behavior estimates and integrate into robot planning methods. We use well-studied models from human motor control based on the speed-accuracy and cost-benefit trade-offs to plan collaborative robot motions. In these models, the human trajectory minimizes an objective function, a formulation we adapt to numerical trajectory optimization. This can then be extended with constraints and new variables to realize collaborative motion planning and goal estimation. We deploy this model, as well as a multi-component movement strategy, in physical collaboration with uncertain goal-reaching and synchronized motion tasks, showing the ability of the approach to produce human-like trajectories over a range of conditions.

Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks.

Based on computational principles, with as yet no direct experi(cid:173) mental validation, it has been proposed that the central nervous system (CNS) uses an internal model to simulate the dynamic be(cid:173) havior of the motor system in planning, control and learning (Sut(cid:173) ton and Barto, 1981; Ito, 1984; Kawato et aI., 1987; Jordan and Rumelhart, 1992; Miall et aI., 1993). We present experimental re(cid:173) sults and simulations based on a novel approach that investigates the temporal propagation of errors in the sensorimotor integration process. Our results provide direct support for the existence of an internal model.

An impedence controlled manipulandum for human movement studies.

An impedence controlled manipulandum for human movement studies.

An impedence controlled manipulandum for human movement studies.