Sidestepping spatial confounds in object-based correspondence effects: The Bimanual Affordance Task (BMAT)

Tucker and Ellis found that when participants made left/right button-presses to indicate whether objects were upright or inverted, responses were faster when the response hand aligned with the task-irrelevant handle orientation of the object. The effect of handle orientation on response times has been interpreted as evidence that individuals perceive grasp affordances when viewing briefly presented objects, which in turn activate grasp-related motor systems. Although the effect of handle alignment has since been replicated, there remains doubt regarding the extent to which the effect is indeed driven by affordance perception. Objects that feature in affordance-compatibility paradigms are asymmetrical and have laterally protruding handles (e.g., mugs) and thus confound spatial and affordance properties. Research has attempted to disentangle spatial compatibility and affordance effects with varying results. In this study, we present a novel paradigm with which to study affordance perception while sidestepping spatial confounds. We use the Bimanual Affordance Task (BMAT) to test whether object affordances in symmetrical objects facilitate response times. Participants ( N = 36) used one of three (left unimanual/right unimanual/bimanual) responses to indicate the colour of presented objects. Objects afforded either a unimanual (e.g., handbag) or a bimanual (e.g., laundry hamper) grasp. Responses were faster when the afforded grasp corresponded with the response type (unimanual vs. bimanual), suggesting that affordance effects exist independent of spatial compatibility.

Visual Grasp Affordance Localization in Point Clouds Using Curved Contact Patches

Detecting affordances on objects is one of the main open problems in robotic manipulation. This paper presents a new method to represent and localize grasp affordances as bounded curved contact patches (paraboloids) of the size of the robotic hand. In particular, given a three-dimensional (3D) point cloud from a range sensor, a set of potential grasps is localized on a detected object by a fast contact patch fitting and validation process. For the object detection, three standard methods from the literature are used and compared. The potential grasps on the object are then refined to a single affordance using their shape (size and curvature) and pose (reachability and minimum torque effort) properties, with respect to the robot and the manipulation task. We apply the proposed method to a circular valve turning task, verifying the ability to accurately and rapidly localize grasp affordances, under significant uncertainty in the environment. We experimentally validate the method with the humanoid robot COMAN on 10 circular control valves fixed on a wall, from five different viewpoints and robot poses for each valve. We compare the reliability of the introduced local grasp affordances method to the baseline that relies only on object detection, illustrating the superiority of ours for the valve turning task.

Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills

SUMMARYParental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called “motionese”7. The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.