This paper describes a set of experiments in which an upper-torso humanoid robot learned to slide a card through a card reader. The small size and the flexibility of the card presented a number of manipulation challenges for the robot. First, because most of the card is occluded by the card reader and the robot's hand during the sliding process, visual feedback is useless for this task. Second, because the card bends easily, it is difficult to distinguish between bending and hitting an obstacle in order to correct the sliding trajectory. To solve these manipulation challenges this paper proposes a method for constraint detection that uses only proprioceptive data. The method uses dynamic joint torque thresholds that are calibrated using the robot's movements in free space. The experimental results show that using this method, the robot can detect when the movement of the card is constrained and modify the sliding trajectory in real time, which makes solving this task possible.

This paper describes an approach to solving insertion tasks by a robot that uses exploratory behaviors and proprioceptive feedback. The approach was inspired by the developmental progression of insertion abilities in both chimpanzees and humans (Hayashi et al. 2006). Before mastering insertions, the infants of the two species undergo a stage where they only press objects against other objects without releasing them. Our goal was to emulate this developmental stage on a robot to see if it may lead to simpler representations for insertion tasks. Experiments were performed using a shapesorter puzzle with three different blocks and holes.

This paper proposes a method for interactive surface recognition and surface categorization by a humanoid robot using a vibrotactile sensory modality. The robot was equipped with an artificial fingernail that had a built-in three-axis accelerometer. The robot interacted with 20 different surfaces by performing five different exploratory scratching behaviors on them. Surface-recognition models were learned by coupling frequency-domain analysis of the vibrations detected by the accelerometer with machine learning algorithms, such as support vector machine (SVM) and k-nearest neighbors (k -NN). The results show that by applying several different scratching behaviors on a test surface, the robot can recognize surfaces better than with any single behavior alone. The robot was also able to estimate a measure of similarity between any two surfaces, which was used to construct a grounded hierarchical surface categorization.

To function in human-inhabited environments a robot must be able to press buttons. There are literally thousands of different buttons, which produce various types of feedback when pressed. This work focuses on doorbell buttons, which provide auditory feedback. Our robot learned to predict if a specific pushing movement would press a doorbell button and produce a sound. The robot explored different buttons with random pushing behaviors and perceived the proprioceptive, tactile, and acoustic outcomes of these behaviors.

This paper describes an approach to interactive object categorization that couples exploratory behaviors and their resulting acoustic signatures to form object categories. The framework was tested with an upper-torso humanoid robot on a container/non-container categorization task. The robot used six exploratory behaviors (drop block, grasp, move, shake, flip, and drop object) and applied them to twenty objects. The results from this large-scale experimental study show that the robot was able to learn meaningful object categories using only acoustic information. The results also show that the quality of the categorization depends on the exploratory behavior used to derive it as some behaviors elicit more salient acoustic signatures than others.