Running across the reality gap: Octopod locomotion evolved in a minimal simulation

Jakobi, Nick

doi:10.1007/3-540-64957-3_63

Cited by 81 publications

(67 citation statements)

References 9 publications

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“…In the walking machines domain, GA-based methods have been used to evolve gaits for a number of legged robots, including hexapods (Barfoot et al, 2006;Gallagher et al, 1996;Lewis et al, 1994) and octopods (Jakobi, 1998;Luk et al, 2001). An evolutionary algorithm has also been successfully applied in the process of developing dynamic gaits for four-legged Sony entertainment robots (Hornby et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

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A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

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Section: Related Workmentioning

confidence: 99%

“…The minimal simulation principle was proposed by Jakobi (1995), who applied it to different robots, including an eight-legged walking machine (Jakobi, 1998). The same approach is also used by Svinin et al (2001), where the minimal simulation model of an octopod is explained in detail.…”

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confidence: 99%

A biologically inspired approach to feasible gait learning for a hexapod robot

Belter¹,

Skrzypczyński²

2010

International Journal of Applied Mathematics and Computer Science

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“…But far fewer have been able to transfer controllers learned in simulation onto real robots and preserve performance Palmer et al, 2009). In other words, the transfer from simulation to reality is not efficient: this is the reality gap problem (Jakobi et al, 1995;Jakobi, 1998). In robotics, the reality gap is overwhelmingly studied in the context of the optimization of controllers in simulation to be transferred on a real robot, in FIGURE 17 | The hardware setup consists of four robots, separated so that they cannot interact with each other.…”

Section: The Reality Gapmentioning

confidence: 99%

Behavioral Diversity Generation in Autonomous Exploration through Reuse of Past Experience

Benureau

Oudeyer

2016

Front. Robot. AI

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The production of behavioral diversity -producing a diversity of effects -is an essential strategy for robots exploring the world when facing situations where interaction possibilities are unknown or non-obvious. It allows to discover new aspects of the environment that cannot be inferred or deduced from available knowledge. However, creating behavioral diversity in situations where it is most crucial -new and unknown ones -is far from trivial. In particular in large and redundant sensorimotor spaces, only small areas are interesting to explore for any practical purpose. When the environment does not provide clues or gradient toward those areas, trying to discover those areas relies on chance. To address this problem, we introduce a method to create behavioral diversity in a new sensorimotor task by re-enacting actions that allowed to produce behavioral diversity in a previous task, along with a measure that quantifies this diversity. We show that our method can learn how to interact with an object by reusing experience from another, that it adapts to instances of morphological changes and of dissimilarity between tasks, and how scaffolding behaviors can emerge by simply switching the attention of the robot to different parts of the environment. Finally, we show that the method can robustly use simulated experiences and crude cognitive models to generate behavioral diversity in real robots.

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“…Many of the now standard test scenarios were developed and extensively explored during this time. These include navigation with obstacle avoidance and target homing [50,51], foraging [7], differential navigation with memory [52], gait learning in legged robots [44,53], coevolution of morphology and control for locomotion [54], and pursuit and evasion, usually formulated in terms of coevolution of populations [38,39,55].…”

Section: Background and Historymentioning

confidence: 99%

Embodied artificial life at an impasse can evolutionary robotics methods be scaled?

Nelson¹

2014

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

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Abstract-Evolutionary robotics (ER) investigates the application of artificial evolution toward the synthesis of robots capable of performing autonomous behaviors. Over the last 25 years, researchers have reported increasingly complex evolved behaviors, and have compiled a de facto set of benchmark tasks. Perhaps the best known of these is the obstacle avoidance and target homing task performed by differential drive robots. More complex tasks studied in recent ER work include augmented variants of the rodent T-maze and complex foraging tasks. But can proof-of-concept results such as these be extended to evolve complex autonomous behaviors in a general sense? In this topical analysis paper we survey relevant research and make the case that common tasks used to demonstrate the effectiveness of evolutionary robotics are not characteristic of more general cases and in fact do not fully prove the concept that artificial evolution can be used to evolve sophisticated autonomous agent behaviors. Robots capable of performing many of the tasks studied in ER have now been evolved using nearly aggregate binary success/fail fitness functions. However, arguments used to support the necessity of incremental methods for complex tasks are essentially sound. This raises the possibility that the tasks themselves allow for relatively simple solutions, or span a relatively small candidate solution set. This paper presents these arguments in detail and concludes with a discussion of current ER research.

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Running across the reality gap: Octopod locomotion evolved in a minimal simulation

Cited by 81 publications

References 9 publications

A biologically inspired approach to feasible gait learning for a hexapod robot

A biologically inspired approach to feasible gait learning for a hexapod robot

Behavioral Diversity Generation in Autonomous Exploration through Reuse of Past Experience

Embodied artificial life at an impasse can evolutionary robotics methods be scaled?

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