Grasping bulky objects with two anthropomorphic hands

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper inclu des some application examples of the proposed approachAccepted versio

Bimanual robotic manipulation based on potential fields

openDual manipulation is a natural skill for humans but not so easy to achieve for a robot. The presence of two end effectors implies the need to consider the temporal and spatial constraints they generate while moving together. Consequently, synchronization between the arms is required to perform coordinated actions (e.g., lifting a box) and to avoid self-collision between the manipulators. Moreover, the challenges increase in dynamic environments, where the arms must be able to respond quickly to changes in the position of obstacles or target objects. To meet these demands, approaches like optimization-based motion planners and imitation learning can be employed but they have limitations such as high computational costs, or the need to create a large dataset. Sampling-based motion planners can be a viable solution thanks to their speed and low computational costs but, in their basic implementation, the environment is assumed to be static. An alternative approach relies on improved Artificial Potential Fields (APF). They are intuitive, with low computational, and, most importantly, can be used in dynamic environments. However, they do not have the precision to perform manipulation actions, and dynamic goals are not considered. This thesis proposes a system for bimanual robotic manipulation based on a combination of improved Artificial Potential Fields (APF) and the sampling-based motion planner RRTConnect. The basic idea is to use improved APF to bring the end effectors near their target goal while reacting to changes in the surrounding environment. Only then RRTConnect is triggered to perform the manipulation task. In this way, it is possible to take advantage of the strengths of both methods. To improve this system APF have been extended to consider dynamic goals and a self-collision avoidance system has been developed. The conducted experiments demonstrate that the proposed system adeptly responds to changes in the position of obstacles and target objects. Moreover, the self-collision avoidance system enables faster dual manipulation routines compared to sequential arm movements

Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition

Bimanual prehension to a solitary target

Grasping and functionally interacting with a relatively large or awkwardly shaped object requires the independent and cooperative coordination of both limbs. Acknowledging the vital role of visual information in successfully executing any prehensile movements, the present study aimed to clarify how well existing bimanual coordination models (Kelso et al, 1979; Marteniuk & Mackenzie, 1980) can account for bimanual prehension movements targeting a single end-point under varying visual conditions. We therefore, employed two experiments in which vision of the target object and limbs was available or unavailable during a bimanual movement in order to determine the affects of visual or memory-guided control (e.g. feedback vs. feed forward) on limb coordination.Ten right-handed participants (mean age = 24.5) performed a specific bimanual prehension movement targeting a solitary, static object under both visual closed loop (CL) and open loop 2s delay (OL2) conditions. Target location was varied while target amplitude remained constant. Kinematic data (bimanual coupling variables) indicated that regardless of target location, participants employed one of two highly successful movement execution strategies depending on visual feedback availability. During visual (CL) conditions participants employed a ‘dominant-hand initiation’ strategy characterized by a significantly faster right-hand (RH) reaction time and simultaneous hand contact with the target. In contrast, when no visual feedback was available (OL2), participants utilized a ‘search and follow’ strategy characterized by limb coupling at movement onset and a reliance on the dominant RH to contact the target ~62 ms before the left.In conclusion, the common goal parameters of targeting a single object with both hands are maintained and successfully achieved regardless of visual condition. Furthermore, independent programming of each limb is undeniably evident within the behaviours observed providing support for the neural cross-talk theory of bimanual coordination (Marteniuk & Mackenzie, 1980). Whether movement execution is visually (CL) or memory-guided (OL2) there is a clear preference of RH utilization possibly due to its dynamic and/or hemispheric advantages in controlling complex motor behaviours (Gonzalez et al., 2006). Therefore, we propose that bimanual grasping to a solitary target is possibly governed globally by a higher-level structure and successful execution is achieved via independent spinal pathway modulation of limbs

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References

This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors

Assessing Performance, Role Sharing, and Control Mechanisms in Human-Human Physical Interaction for Object Manipulation

abstract: Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Identification and Intervention for Action Planning Deficits in Children With Hemiplegic Cerebral Palsy

The primary purpose of this investigation was to describe and quantify action-planning deficits during goal-directed movements in children with hemiplegic cerebral palsy (HCP). Three specific topics were addressed: brain activation, kinematics, and the use of visual input. First, we assessed prefrontal cortex (PFC) activation during complex goal-directed actions in children with HCP. The outcome suggested that children with HCP have higher PFC activation than age matched typically developing (TD) children during action planning, potentially due to the difficulty in allocating attentional resources for simultaneously processing the cognitive (i.e., attention, memory, information processing) and motor demands of the goal-directed task. Reduced task performance paralleled the increased cortical activation. Secondly, we explored the kinematics of action planning and execution of goal-directed action of children with HCP. We found that children with HCP lack forward planning capacity of sequential action, which further impacts the ability to execute action. Thirdly, we explored anticipatory visual patterns and the temporal coupling between eye and hand in children with HCP. The outcomes from this study indicate delays in anticipatory vision and impaired visuomotor coordination, potential factors responsible for the delay in motor performance in children with HCP. Moreover, we observed increased visual monitoring of the moving arm, a potential compensatory mechanism for impaired proprioception of the arm. A secondary purpose was to evaluate whether hand arm bimanual intensive therapy (HABIT) improves action planning and subsequent action execution deficits, and improves PFC activation. After completion of 50-hours of HABIT program, children with HCP displayed reduction in PFC activation. The reduction in cortical activation was accompanied by clinically relevant improvements in bimanual coordination, affected hand function, and motor task performance. Altogether this investigation provides novel information about the action planning and subsequent action execution deficits and the influence of therapeutic interventions in reducing these deficits to optimize learning motor skills in children with HCP

Looking ahead: anticipatory gaze and motor ability in infancy

The present study asks when infants are able to selectively anticipate the goals of observed actions, and how this ability relates to infants' own abilities to produce those specific actions. Using eye-tracking technology to measure on-line anticipation, 6-, 8- and 10-month-old infants and a control group of adults were tested while observing an adult reach with a whole hand grasp, a precision grasp or a closed fist towards one of two different sized objects. The same infants were also given a comparable action production task. All infants showed proactive gaze to the whole hand grasps, with increased degrees of proactivity in the older groups. Gaze proactivity to the precision grasps, however, was present from 8 months of age. Moreover, the infants' ability in performing precision grasping strongly predicted their ability in using the actor's hand shape cues to differentially anticipate the goal of the observed action, even when age was partialled out. The results are discussed in terms of the specificity of action anticipation, and the fine-grained relationship between action production and action perception

Development of an intelligent object for grasp and manipulation research

Kõiva R, Haschke R, Ritter H. Development of an intelligent object for grasp and manipulation research. Presented at the ICAR 2011, Tallinn, Estonia.In this paper we introduce a novel device, called iObject, which is equipped with tactile and motion tracking sensors that allow for the evaluation of human and robot grasping and manipulation actions. Contact location and contact force, object acceleration in space (6D) and orientation relative to the earth (3D magnetometer) are measured and transmitted wirelessly over a Bluetooth connection. By allowing human-human, human-robot and robot-robot comparisons to be made, iObject is a versatile tool for studying manual interaction. To demonstrate the efficiency and flexibility of iObject for the study of bimanual interactions, we report on a physiological experiment and evaluate the main parameters of the considered dual-handed manipulation task