This research illustrates the design, implementation, and evaluation of pneumatic variable-stiffness actuator (VSA) used to strum a four-stringed ukulele with audio variability. A guitar pick is antagonistically loaded with two inflatable polydimethylsiloxane (PDMS) actuators, allowing for the independent control of both position or stiffness through the utilization of one and two PDMS solenoids, respectively. To generate smooth analog pressure signals, the bellows incorporate a controlled leak to atmospheric pressure, having synonymous properties to a low-pass filter circuit when fed a coarse pressure signal through PWM control. Experimental results illustrate a minimum to maximum stiffness range of 76 to 320 Nmm/rad, a maximum cyclical speeds of 2.04 Hz, and a stiffness change rate of 96 Nmm/rad/kPa. Additional to the VSA, alternative PDMS fabrication methods are presented to allow for complex, precise manufacturing of silicone bodies in a low-cost manner.

In this video presentation we introduce a 50 degrees of freedom humanoid robot, CB -ComputationalBrain[1]. CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real world contexts, as humans do. In so doing, we focus on utilising a system that is closer to humans - in sensing, kinematics configuration and performance. We present a full-body compliance controller that was developed for the motion control of our humanoid robot [2]. Our initial experimentation on our system includes: 1) full-body compliant control - physical interactions/balancing/motion control; 2) the integrated visual ocular-motor responses; 3) perception and control - reaching, foveation, and active object recognition; 4) our studies of Central Pattern Generator for walking.

We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center of pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We applied the proposed control approach to our newly developed human sized humanoid robot and a small size humanoid robot developed by Sony, enabling them to generate successful stepping and walking patterns

This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize across tasks and global features to learn from practice. We demonstrate this approach applied to the marble maze task. Our robot uses local features to initially learn primitive selection and subgoal generation policies from observing a teacher maneuver a marble through a maze. The robot then uses this information as it tries to traverse another maze, and refines the information during learning from practice

This paper focuses on learning to select behavioral primitives and generate sub-goals from practicing a task. We present a novel algorithm that combines Q-learning and a locally weighted learning method to improve primitive selection and sub-goal generation. We demonstrate this approach applied to the tilt maze task. Our robot initially learns to perform this task using learning from observation, and then learns from practice.

This paper describes humanoid robot learning from observation and game playing using information provided by a real-time PC-based vision system. To cope with extremely fast motions that arise in the environment, a visual system capable of perceiving the motion of several objects at 60 fields per second was developed. We have designed a suitable error recovery scheme for our vision system to ensure successful game playing over longer periods of time. To increase the learning rate of the robot it is given domain knowledge in the form of primitives. The robot learns how to perform primitives from data collected while observing a human. The robot control system and primitive use strategy are also explained.

This paper describes the rise of task primitives in robot learning from observation. A framework is developed that uses observed data to initially learn a task and the agent then goes on to increase its performance through repeated task performance (learning from practice). Data that is collected while the human performs a task is parsed into small parts of the task called primitives. Modules are created for each primitive that encode the movements required during the performance of the primitive, and when and where the primitives are performed. The feasibility of this method is currently being tested with agents that learn to play a virtual and an actual air hockey game.