From human-intention recognition to compliant control using dynamical systems in physical human-robot interaction

Human ability to coordinate one's actions with other individuals to perform a task together is fascinating. For example, we coordinate our action with others when we carry a heavy object or when we construct a piece of furniture. Capabilities such as (1) force/compliance adaptation, (2) intention recognition, and (3) action/motion prediction enables us to assist others and fulfill the task. For instance, by adapting the compliance, we not only reject undesirable perturbations that undermine the task but also incorporate others' motions into the interaction. Complying with partners' motions allows us to recognize their intention and consequently predict their actions. With the growth of factories involving humans and robots working side by side, designing controllers and algorithms with such capacities is a crucial step toward assistive robotics. The challenge, however, is to attain a unified control strategy with predictive/adaptive capacities at the task, motion, and force-level which ensures a stable and safe interaction. To this aim, this thesis proposes a state-dependent dynamical system-based approach for prediction and control in physical human-robot interactions.

In the first part of this dissertation, we focus on the human capacity to predict their partners' motion. More specifically, we investigate mechanisms of spatio-temporal coordination between two partners. We employ a simple scenario called âthe mirror gameâ where two individuals (human, robot, or avatar) imitate each other's motions. Our empirical assessment reveal that the intention-based prediction of the leader's motions allows the follower to compensate for perception-action delays and to improve the tracking performance in terms of temporal coordination and confidence.

In the second part of this dissertation, we propose an adaptive mechanism that enables the robot to recognize the intention of a human user. We utilize state-dependent dynamical systems for motion planning and impedance control to deliver safe and compliant human-robot interaction. We consider a series of tasks (possible human intentions) encoded by dynamical system. Applying a similarity metric between the real velocities (induced by the human) and desired velocities generated by the dynamical systems, the robot is thus able to recognize the human's intention and switch to the intended task. We provide a rigorous experimental and analytical evaluation of our method yielding an interaction behavior that is safe and intuitive for the human.

Finally, we tackle the compliance adaptation capability. We propose an admittance controller that reacts only when human-intentional forces are detected. Intentional and accidental forces are distinguished by measuring the persistency of the external forces, through a computation of the autocorrelation/energy of the force patterns. The overall controller exhibits variable stiffness where high stiffness allows the robot to reject the external disturbances and execute the task autonomously whereas low stiffness enables the robot to comply with human intentional forces. We demonstrate that our control architecture is effective in delivering satisfactory tracking and compliant behavior through a series of robotic experimentations.

Imitation Learning of Motion Coordination in Robots

Elena Gribovskaya

The ease with which humans coordinate all their limbs is fascinating. Such a simplicity is the result of a complex process of motor coordination, i.e. the ability to resolve the biomechanical redundancy in an efficient and repeatable manner. Coordination enables a wide variety of everyday human activities from filling in a glass with water to pair figure skating. Therefore, it is highly desirable to endow robots with similar skills. Despite the apparent diversity of coordinated motions, all of them share a crucial similarity: these motions are dictated by underlying constraints. The constraints shape the formation of the coordination patterns between the different degrees of freedom. Coordination constraints may take a spatio-temporal form; for instance, during bimanual object reaching or while catching a ball on the fly. They also may relate to the dynamics of the task; for instance, when one applies a specific force profile to carry a load. In this thesis, we develop a framework for teaching coordination skills to robots. Coordination may take different forms, here, we focus on teaching a robot intra-limb and bimanual coordination, as well as coordination with a human during physical collaborative tasks. We use tools from well-established domains of Bayesian semiparametric learning (Gaussian Mixture Models and Regression, Hidden Markov Models), nonlinear dynamics, and adaptive control. We take a biologically inspired approach to robot control. Specifically, we adopt an imitation learning perspective to skill transfer, that offers a seamless and intuitive way of capturing the constraints contained in natural human movements. As the robot is taught from motion data provided by a human teacher, we exploit evidence from human motor control of the temporal evolution of human motions that may be described by dynamical systems. Throughout this thesis, we demonstrate that the dynamical system view on movement formation facilitates coordination control in robots. We explain how our framework for teaching coordination to a robot is built up, starting from intra-limb coordination and control, moving to bimanual coordination, and finally to physical interaction with a human. The dissertation opens with the discussion of learning discrete task-level coordination patterns, such as spatio-temporal constraints emerging between the two arms in bimanual manipulation tasks. The encoding of bimanual constraints occurs at the task level and proceeds through a discretization of the task as sequences of bimanual constraints. Once the constraints are learned, the robot utilizes them to couple the two dynamical systems that generate kinematic trajectories for the hands. Explicit coupling of the dynamical systems ensures accurate reproduction of the learned constraints, and proves to be crucial for successful accomplishment of the task. In the second part of this thesis, we consider learning one-arm control policies. We present an approach to extracting non-linear autonomous dynamical systems from kinematic data of arbitrary point-to-point motions. The proposed method aims to tackle the fundamental questions of learning robot coordination: (i) how to infer a motion representation that captures a multivariate coordination pattern between degrees of freedom and that generalizes this pattern to unseen contexts; (ii) whether the policy learned directly from demonstrations can provide robustness against spatial and temporal perturbations. Finally, we demonstrate that the developed dynamical system approach to coordination may go beyond kinematic motion learning. We consider physical interactions between a robot and a human in situations where they jointly perform manipulation tasks; in particular, the problem of collaborative carrying and positioning of a load. We extend the approach proposed in the second part of this thesis to incorporate haptic information into the learning process. As a result, the robot adapts its kinematic motion plan according to human intentions expressed through the haptic signals. Even after the robot has learned the task model, the human still remains a complex contact environment. To ensure robustness of the robot behavior in the face of the variability inherent to human movements, we wrap the learned task model in an adaptive impedance controller with automatic gain tuning. The techniques, developed in this thesis, have been applied to enable learning of unimanual and bimanual manipulation tasks on the robotics platforms HOAP-3, KATANA, and i-Cub, as well as to endow a pair of simulated robots with the ability to perform a manipulation task in the physical collaboration.

EPFL2012

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Aude Billard, Karl Gustav Henriks, Mahdi Khoramshahi, Seyed Sina Mirrazavi Salehian, Aileen Charlotte Naef

Many manipulation tasks require coordinated motions for arm and fingers. Complexity increases when the task requires to control for the force at contact against a non-flat surface; This becomes even more challenging when this contact is done on a human. All these challenges are regrouped when one, for instance, massages a human limb. When massaging, the robotic arm is required to continuously adapt its orientation and distance to the limb while the robot fingers exert desired patterns of forces and motion on the skin surface. To address these challenges, we adopt a Dynamical System (DS) approach that offers a unified motion-force control approach and enables to easily coordinate multiple degrees of freedom. As each human limb may slightly differ, we learn a model of the surface using support vector regression (SVR) which enable us to obtain a distance-to-surface mapping. The gradient of this mapping, along with the DS, generates the desired motions for the interaction with the surface. A DS-based impedance control for the robotic fingers allows to control separately for force along the normal direction of the surface while moving in the tangential plane. We validate our approach using the KUKA IIWA robotic arm and Allegro robotic hand for massaging a mannequin arm covered with a skin-like material.We show that our approach allows for 1) reactive motion planning to reach for an unknown surface, 2) following desired motion patterns on the surface, and 3) exerting desired interaction forces profiles. Our results show the effectiveness of our approach; especially the robustness toward uncertainties for shape and the given location of the surface.

2020

Embodied Flight with a Drone

Alexandre Thierry Cherpillod, Dario Floreano, Stefano Mintchev

Most human-robot interfaces, such as joysticks and keyboards, require training and constant cognitive effort and provide a limited degree of awareness of the robots’ state and its environment. Embodied interactions, that is the bidirectional link between the physical bodies and control systems of the robot and of the human, could not only enable a more intuitive control of robots, even for novices, but also provide users with more immersive sensations. But providing an embodied interaction by mapping human movements into a non-anthropomorphic robot is particularly challenging. In this paper, we describe a natural and immersive embodied interaction that allows users to control and experience drone flight with their own bodies. The setup uses a commercial flight simulator that tracks hand movements and provides haptic and visual feedback. The paper discusses how to map body movement with drone motion, and how the resulting embodied interaction provides a more natural and immersive flight experience to unskilled users with respect to a conventional RC remote controller.

2019

Imitation Learning of Motion Coordination in Robots

Elena Gribovskaya

EPFL2012