Optimization of humanoid walking controller: Crossing the reality gap

Oliveira, Miguel; Doncieux, Stéphane; Mouret, Jean-Baptiste; Santos, Cristina P.

doi:10.1109/humanoids.2013.7029963

Cited by 7 publications

(7 citation statements)

References 33 publications

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“…A similar approach was used when comparing multi-objective evolutionary strategies and genetic algorithms, in which a robot was required to display both collision-free navigation and object retrieval abilities [ 44 ]. Another relevant study concerns the evolution of a walking controller for a humanoid robot [ 45 , 46 ]. In that study, several proxies are introduced that reward specific characteristics of the desired gait, still the real objective is efficient locomotion.…”

Section: Modes Of Problem Solving With Moo In Evolutionary Roboticsmentioning

confidence: 99%

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

Trianni

López-Ibáñez

2015

PLoS ONE

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The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

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Section: Modes Of Problem Solving With Moo In Evolutionary Roboticsmentioning

confidence: 99%

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

Trianni

López-Ibáñez

2015

PLoS ONE

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“…The advantages of the transferability approach is that evolution occurs in simulation but the evolutionary process is driven towards solutions that are likely to work on the physical robot. In recent experiments, this approach led to the successful evolution of walking controllers for quadruped (Koos et al, 2013b), hexapod (Koos et al, 2013a), and biped robots (Oliveira et al, 2013), with no more than 25 tests on the physical robot. We evaluate our algorithm on two sets of experiments.…”

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confidence: 99%

Evolving a Behavioral Repertoire for a Walking Robot

Cully

Mouret

2016

Evolutionary Computation

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Numerous algorithms have been proposed to allow legged robots to learn to walk. However, most of these algorithms are devised to learn walking in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which combines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of controllers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution introduced a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

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“…This predicted transferability is used as a new objective in a multi-objective EA alongside other objectives so that generated solutions tend to behave the same in simulation and in reality. The approach has been applied to a quadruped [98,97] and biped [142] locomotion tasks as well as to a T-maze navigation task [98]. As the number of evaluations on the real robot is reduced, this approach is considered to also address the reducing evaluation challenge.…”

Section: Reality Gapmentioning

confidence: 99%

“…While still keeping a constant simulation, another approach consists of evaluating several solutions directly on the real robot [98,97,133,142]. Relying on the hypothesis that reasonably good simulators do indeed exist, the approach proposes learning a model of behavior discrepancies between simulation and reality in order to avoid the most unrealistic behaviors.…”

Section: Reality Gapmentioning

confidence: 99%

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Doncieux

Mouret

2014

Evol. Intel.

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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two subcategories are further considered: task-specific approaches, which require knowledge on how to solve the task and taskagnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

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Optimization of humanoid walking controller: Crossing the reality gap

Cited by 7 publications

References 33 publications

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

Evolving a Behavioral Repertoire for a Walking Robot

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

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