Generación de trayectorias para un robot bípedo en fase de balanceo a partir de captura de movimiento humano

This paper proposes human motion capture to generate movements for the right leg in swing phase of a biped robot restricted to the sagittal plane -- Such movements are defined by time functions representing the desired angular positions for the joints involved -- Motion capture performed with a Microsoft Kinect TM camera and from the data obtained joint trajectories were generated to control the robot’s right leg in swing phase -- The proposed control law is a hybrid strategy; the first strategy is based on a computed torque control to track reference trajectories, and the second strategy is based on time scaling control ensuring the robot’s balance -- This work is a preliminary study to generate humanoid robot trajectories from motion captureEn este trabajo se propone la captura de movimiento humano para generar movimientos de la pierna derecha en fase de oscilación de un robot bípedo restringido al plano sagital -- Estos movimientos son definidos mediante funciones de tiempo que representan las posiciones angulares deseadas para las articulaciones involucradas -- La captura de movimiento realiza con un sensor Kinect TM y a partir de los datos obtenidos se generaron trayectorias articulares para controlar la pierna derecha del robot en la fase de balanceo -- La ley de control propuesta es una estrategia híbrida; la primera estrategia se basa en un control por par calculado para realizar un seguimiento de trayectorias de referencia, y la segunda estrategia se basa en un control por escalado de tiempo para garantizar el equilibrio del robot -- Este trabajo es un estudio preliminar para generar trayectorias de robots humanoides a partir de captura de movimient

Efficient Walking Gait Generation via Principal Component Representation of Optimal Trajectories: Application to a Planar Biped Robot With Elastic Joints

Recently, the method of choice to exploit robot dynamics for efficient walking is numerical optimization (NO). The main drawback in NO is the computational complexity, which strongly affects the time demand of the solution. Several strategies can be used to make the optimization more treatable and to efficiently describe the solution set. In this letter, we present an algorithm to encode effective walking references, generated offline via numerical optimization, extracting a limited number of principal components and using them as a basis of optimal motions. By combining these components, a good approximation of the optimal gaits can be generated at run time. The advantages of the presented approach are discussed, and an extensive experimental validation is carried out on a planar legged robot with elastic joints. The biped thus controlled is able to start and stop walking on a treadmill, and to control its speed dynamically as the treadmill speed change

Online adaptation of reference trajectories for the control of walking systems

International audienceA simple and widely used way to make a robotic system walk without falling is to make it track a reference tra jectory in one way or another, but the stability obtained this way may be limited and even small perturbations may lead to a fall. We propose here a series of heuristics to improve the stability that can be obtained from such a tracking control law, through an online adaptation of the choice of the reference tra jectory being tracked. Encouraging simulations are obtained in the end on a simple planar biped model

Staged Contact Optimization: Combining Contact-Implicit and Multi-Phase Hybrid Trajectory Optimization

Trajectory optimization problems for legged robots are commonly formulated with fixed contact schedules. These multi-phase Hybrid Trajectory Optimization (HTO) methods result in locally optimal trajectories, but the result depends heavily upon the predefined contact mode sequence. Contact-Implicit Optimization (CIO) offers a potential solution to this issue by allowing the contact mode to be determined throughout the trajectory by the optimization solver. However, CIO suffers from long solve times and convergence issues. This work combines the benefits of these two methods into one algorithm: Staged Contact Optimization (SCO). SCO tightens constraints on contact in stages, eventually fixing them to allow robust and fast convergence to a feasible solution. Results on a planar biped and spatial quadruped demonstrate speed and optimality improvements over CIO and HTO. These properties make SCO well suited for offline trajectory generation or as an effective tool for exploring the dynamic capabilities of a robot

Benchmarking Deep Reinforcement Learning for Continuous Control

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201