An adaptive compliance Hierarchical Quadratic Programming controller for ergonomic human–robot collaboration

This paper proposes a novel Augmented Hierarchical Quadratic Programming (AHQP) framework for multi-tasking control in Human-Robot Collaboration (HRC) which integrates human-related parameters to optimize ergonomics. The aim is to combine parameters that are typical of both industrial applications (e.g. cycle times, productivity) and human comfort (e.g. ergonomics, preference), to identify an optimal trade-off. The augmentation aspect avoids the dependency from a fixed end-effector reference trajectory, which becomes part of the optimization variables and can be used to define a feasible workspace region in which physical interaction can occur. We then demonstrate that the integration of the proposed AHQP in HRC permits the addition of human ergonomics and preference. To achieve this, we develop a human ergonomics function based on the mapping of an ergonomics score, compatible with AHQP formulation. This allows to identify at control level the optimal Cartesian pose that satisfies the active objectives and constraints, that are now linked to human ergonomics. In addition, we build an adaptive compliance framework that integrates both aspects of human preferences and intentions, which are finally tested in several collaborative experiments using the redundant MOCA robot. Overall, we achieve improved human ergonomics and health conditions, aiming at the potential reduction of work-related musculoskeletal disorders

On the role of robot configuration in Cartesian stiffness control

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior

A Method for Autonomous Robotic Manipulation through Exploratory Interactions with Uncertain Environments

Expanding robot autonomy can deliver functional flexibility and enable fast deployment of robots in challenging and unstructured environments. In this direction, significant advances have been recently made in visual-perception driven autonomy, which is mainly due to the availability of rich sensory data-sets. However, current robots’ physical interaction autonomy levels still remain at a basic level. Towards providing a systematic approach to this problem, this paper presents a new context-aware and adaptive method that allows a robotic platform to interact with unknown environments. In particular, a multi-axes self-tuning impedance controller is introduced to regulate quasi-static parameters of the robot based on previous experience in interacting with similar environments and the real-time sensory data. The proposed method is also capable of differentiating internal and external disruptions, and responding to them accordingly and appropriately. An agricultural experiment with different deformable material is presented to validate robot interaction autonomy improvements, and the capability of the proposed methodology in detecting and responding to unexpected events (e.g., faults)

Design of an Energy-Aware Cartesian Impedance Controller for Collaborative Disassembly

Human-robot collaborative disassembly is an emerging trend in the sustainable recycling process of electronic and mechanical products. It requires the use of advanced technologies to assist workers in repetitive physical tasks and deal with creaky and potentially damaged components. Nevertheless, when disassembling worn-out or damaged components, unexpected robot behaviors may emerge, so harmless and symbiotic physical interaction with humans and the environment becomes paramount. This work addresses this challenge at the control level by ensuring safe and passive behaviors in unplanned interactions and contact losses. The proposed algorithm capitalizes on an energy-aware Cartesian impedance controller, which features energy scaling and damping injection, and an augmented energy tank, which limits the power flow from the controller to the robot. The controller is evaluated in a real-world flawed unscrewing task with a Franka Emika Panda and is compared to a standard impedance controller and a hybrid force-impedance controller. The results demonstrate the high potential of the algorithm in human-robot collaborative disassembly tasks.Comment: 7 pages, 6 figures, presented at the 2023 IEEE International Conference on Robotics and Automation (ICRA). Video available at https://www.youtube-nocookie.com/embed/SgYFHMlEl0

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences, encoding at the same time motion patterns and context. Additionally, the method introduces an event-based automatic video segmentation and clustering, which allows to group similar events, detecting also on the fly if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.Comment: 8 pages, 8 figures, submitted to IEEE RAS International Symposium on Robot and Human Interactive Communication (RO-MAN), for associated video see https://youtu.be/Ftu_EHAtH4

Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences, encoding at the same time motion patterns and context. Additionally, the method introduces an event-based automatic video segmentation and clustering, which allows to group similar events, detecting also on the fly if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects

Human-Like Impedance and Minimum Effort Control for Natural and Efficient Manipulation

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human’s estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulatio

A reduced-complexity description of arm endpoint stiffness with applications to teleimpedance control

Effective and stable execution of a remote manipulation task in an uncertain environment requires that the task force and position trajectories of the slave robot be appropriately commanded. To achieve this goal, in teleimpedance control, a reference command which consists of the stiffness and position profiles of the master is computed and realized by the compliant slave robot in real-time. This highlights the need for a suitable and computationally efficient tracking of the human limb stiffness profile in real-time. In this direction, based on the observations in human neuromotor control which give evidence on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the role of muscular co-activations which contribute to a coordinated regulation of the task stiffness in all directions, we propose a novel and computationally efficient model of the arm endpoint stiffness behaviour. Real-time tracking of the human arm kinematics is achieved using an arm triangle monitored by three markers placed at the shoulder, elbow and wrist level. In addition, a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and co-contraction levels of the chosen muscles. Results of this study suggest that the proposed model enables the master to naturally execute a remote task by modulating the direction of the major axes of the endpoint stiffness and its volume using arm configuration and the co-activation of the involved muscles, respectively

Robot Trajectory Adaptation to Optimise the Trade-off between Human Cognitive Ergonomics and Workplace Productivity in Collaborative Tasks

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploits the generation of B-spline trajectories in the joint space and formulation of a multi-objective optimisation problem to online adjust the total execution time and smoothness of the robot trajectories. The former ensures human efficiency and productivity of the workplace, while the latter contributes to safeguarding the user's comfort and cognitive ergonomics. The performance of the proposed framework was evaluated in a typical industrial task. Results demonstrated its capability to enhance the productivity of the human-robot dyad while mitigating the cognitive workload induced in the worker