Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

Endo, Gen; Morimoto, Jun; Matsubara, Takamitsu; Nakanishi, Jun; Cheng, Gordon

doi:10.1177/0278364907084980

Cited by 191 publications

(135 citation statements)

References 25 publications

Supporting

Mentioning

135

Contrasting

Order By: Relevance

“…However, finding the correct coupling weights to generate a desired gait is a difficult task. Hence, some researchers focused on the design of architectures for CPGs constructed by coupled oscillators, and in this way succeeded to generate common gaits in legged robots such as bipeds [13][14], and quadrupeds [15][16][17], even in legless robots such as fish [18], and snake robots [19][20].…”

Section:  Applications Of Cpg-based Control In Roboticsmentioning

confidence: 99%

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

2017

View full text Add to dashboard Cite

Abstract:In this paper, we intend to improve the CPG network presented by Pinto et al. based on 4-cell model for bipedal locomotion systems. This model is composed of four coupled identical cells which internal dynamics of each one is described by the Morris-Lecar nonlinear differential equation and the couplings between the cells follow the synaptic type. We exploit an elitist non-dominated sorting genetic algorithm (NSGA II) to find the best set of coupling weights by which the phase differences become optimally close to the ones required for a primary bipedal gait. Thus, we achieve the rhythmic signals associated with four primary bipedal gaits of walk, run, two-legged jump, and two-legged hop. Also, we successfully obtain all secondary gaits corresponding to the bipedal locomotion identified by Pinto et al. from the 4-cell model, by symmetry breaking bifurcations of primary gaits. Particularly, we are able to produce the secondary gait called "hesitation walk" through transition from primary gaits of run and two-legged jump.

show abstract

Section:  Applications Of Cpg-based Control In Roboticsmentioning

confidence: 99%

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

2017

View full text Add to dashboard Cite

show abstract

“…Both posture control and biped locomotion are challenging research topics for a human-like and autonomous humanoid robot. Our exploration focused on biologically inspired control algorithms for locomotion using three different humanoid robots (DB-chan [72,76,77], Fujitsu Automation HOAP-2 [48] and CB-i [78]) as well as a small humanoid robot provided by the SONY Corp. [79].…”

Section: Creating a Brain By Brain-motivated Humanoid-motor-learning mentioning

confidence: 99%

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto¹,

Kawato

2015

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

show abstract

“…For rhythmic behaviors half-elliptical locuses have been used as a representation of the gait pattern of a robot dog [Kohl and Stone, 2004]. Neural Networks: Instead of analytically describing rhythmic movements, neural networks can be used as oscillators to learn gaits of a a two legged robot [Geng et al, 2006, Endo et al, 2008. Also a peg-in-hole (see Figure 2.1b) and a ball-balancing task as well as a navigation task [Hailu and Sommer, 1998] have been learned with neural networks as policy function approximators.…”

Section: Pre-structured Policiesmentioning

confidence: 99%

Learning motor skills: from algorithms to robot experiments

Kober

Peters

2014

It - Information Technology

View full text Add to dashboard Cite

Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 2.0 Deutschland http://creativecommons.org/licenses/by-nc-nd/2.0/de/ Abstract Ever since the word "robot" was introduced to the English language by KarelČapek's play "Rossum's Universal Robots" in 1921, robots have been expected to become part of our daily lives. In recent years, robots such as autonomous vacuum cleaners, lawn mowers, and window cleaners, as well as a huge number of toys have been made commercially available. However, a lot of additional research is required to turn robots into versatile household helpers and companions. One of the many challenges is that robots are still very specialized and cannot easily adapt to changing environments and requirements. Since the 1960s, scientists attempt to provide robots with more autonomy, adaptability, and intelligence. Research in this field is still very active but has shifted focus from reasoning based methods towards statistical machine learning. Both navigation (i.e., moving in unknown or changing environments) and motor control (i.e., coordinating movements to perform skilled actions) are important sub-tasks.In this thesis, we will discuss approaches that allow robots to learn motor skills. We mainly consider tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The presented tasks correspond to sports and games but the presented techniques will also be applicable to more mundane household tasks. Motor skills can often be represented by motor primitives. Such motor primitives encode elemental motions which can be generalized, sequenced, and combined to achieve more complex tasks. For example, a forehand and a backhand could be seen as two different motor primitives of playing table tennis. We show how motor primitives can be employed to learn motor skills on three different levels. First, we discuss how a single motor skill, represented by a motor primitive, can be learned using reinforcement learning. Second, we show how such learned motor primitives can be generalized to new situations. Finally, we present first steps towards using motor primitives in a hierarchical setting and how several motor primitives can be combined to achieve more complex tasks.To date, there have been a number of successful applications of learning motor primitives employing imitation learning. However, many interesting motor learning problems are high-dimensional reinforcement learning problems which are often beyond the reach of current reinforcement learning methods. We review research on reinforcement learning applied to robotics and point out key challenges and important strategies to render reinforcement learning tractable. Based on these insights, we introduce novel learning approaches both for single and generalized motor skills.For learning single motor skills, we study parametrized policy search methods and introduce a framework of reward-weighted imi...

show abstract

Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

Cited by 191 publications

References 25 publications

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Learning motor skills: from algorithms to robot experiments

Contact Info

Product

Resources

About