José Rosado

A Kinect-Based Motion Capture System for Robotic Gesture Imitation

Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates and evaluates the use of a Kinect-based capture system that estimates the 3D human poses and converts them into gestures imitation in a robot. The main objectives are twofold: (1) to improve the initially estimated poses through a correction method based on constraint optimization, and (2) to present a method for computing the joint angles for the upper limbs corresponding to motion data from a human demonstrator. The feasibility of the approach is demonstrated by experimental results showing the upper-limb imitation of human actions by a robot model.

Reproduction of human arm movements using Kinect-based motion capture data

Transferring skills from humans to robots is an appealing way for teaching artificial systems to perform a variety of different tasks. In this context, imitation learning appears as an important approach for teaching robots due to the generation of human-like movements and the ease of teaching new tasks. This paper addresses the use of a Kinect-based human motion capture system and the reproduction of arm movements in an upper-body humanoid robot. The objectives are threefold: (1) to deal with the lack of a kinematics model that assure coherence in the recorded 3D human poses; (2) to explore the inclusion of a shoulder complex based on a parallel mechanism; and (3) to demonstrate and evaluate how two robot models can be used to reproduce the human demonstrations. Several experimental results are included showing the upper-limb reproduction of human arm movements.

Adaptive Robot Biped Locomotion with Dynamic Motion Primitives and Coupled Phase Oscillators

In order to properly function in real-world environments, the gait of a humanoid robot must be able to adapt to new situations as well as to deal with unexpected perturbations. A promising research direction is the modular generation of movements that results from the combination of a set of basic primitives. In this paper, we present a robot control framework that provides adaptive biped locomotion by combining the modulation of dynamic movement primitives (DMPs) with rhythm and phase coordination. The first objective is to explore the use of rhythmic movement primitives for generating biped locomotion from human demonstrations. The second objective is to evaluate how the proposed framework can be used to generalize and adapt the human demonstrations by adjusting a few open control parameters of the learned model. This paper contributes with a particular view into the problem of adaptive locomotion by addressing three aspects that, in the specific context of biped robots, have not received much attention. First, the demonstrations examples are extracted from human gaits in which the human stance foot will be constrained to remain in flat contact with the ground, forcing the “bent-knee” at all times in contrast with the typical straight-legged style. Second, this paper addresses the important concept of generalization from a single demonstration. Third, a clear departure is assumed from the classical control that forces the robot’s motion to follow a predefined fixed timing into a more event-based controller. The applicability of the proposed control architecture is demonstrated by numerical simulations, focusing on the adaptation of the robot’s gait pattern to irregularities on the ground surface, stepping over obstacles and, at the same time, on the tolerance to external disturbances.

Biped Walking Learning from Imitation Using Dynamic Movement Primitives

Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates the use of a VICON system to capture human locomotion that is used to train a set of Dynamic Movement Primitives. These DMP can then be used to directly control a humanoid robot on the task space. The main objectives of this paper are: (1) to study the main characteristics of human natural locomotion and human “robot-like” locomotion; (2) to use the captured motion to train a DMP; (3) to use the DMP to directly control a humanoid robot in task space. Numerical simulations performed on V-REP demonstrate the effectiveness of the proposed solution.

Adaptation of Robot Locomotion Patterns with Dynamic Movement Primitives

Functional locomotion requires continuous modulation of coordination within and between legs to flexibly accommodate demands of real-word environments. In this context, dynamic movement primitives (DMP) is a powerful tool for motion planning based on demonstrations, being used as a compact policy representation well-suited for robot learning. In this work, we study on-line adaptation of robot biped locomotion patterns when employing DMP as trajectory representations. Here, the adaptation of learned walking movements is obtained from a single demonstration. The goal is to demonstrate and evaluate how new movements can be generated by simply modifying the parameters of rhythmic DMP learned in task space. The formulation in task space allows recreating new movements such that the DMPs parameters directly relate to task variables, such as step length, hip height, foot clearance and forward velocity. Several experiments are conducted using the V-REP robotics simulator, including the adaptation of the robots gait pattern to irregularities on the ground surface and stepping over obstacles.

Adaptive Behavior of a Biped Robot Using Dynamic Movement Primitives

Over the past few years, several studies have suggested that adaptive behavior of humanoid robots can arise based on phase resetting embedded in pattern generators. In this paper, we propose a movement control approach that provides adaptive behavior by combining the modulation of dynamic movement primitives (DMP) and interlimb coordination with coupled phase oscillators. Dynamic movement primitives (DMP) represent a powerful tool for motion planning based on demonstration examples. This approach is currently used as a compact policy representation well-suited for robot learning. The main goal is to demonstrate and evaluate the role of phase resetting based on foot-contact information in order to increase the tolerance to external perturbations. In particular, we study the problem of optimal phase shift in a control system influenced by delays in both sensory information and motor actions. The study is performed using the V-REP simulator, including the adaptation of the humanoid robot’s gait pattern to irregularities on the ground surface.

Motion generalization from a single demonstration using dynamic primitives

In recent years, several studies have suggested that improved performance of modern robots can arise from encoding motor commands in terms of dynamic primitives. In this context, dynamic movement primitives (DMPs) have been proposed as a powerful tool for motion planning based on demonstrated examples. In this work, we focus on generalizing discrete and periodic movements from a single demonstration. Here, we argue that geometric invariance in itself may be useful to provide an initial representation of movements in an incremental process of learning from experience. The purpose of the current study is to portray the generalization performance of this approach, both using simulated and human motion capture data. The generalization performance is evaluated and the feasibility of the approach is discussed.