Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ``What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of `morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: `newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.Comment: preprint-nature scientific report. arXiv admin note: text overlap with arXiv:2303.1259

Lexicase Selection for Multi-Task Evolutionary Robotics

In Evolutionary Robotics, Lexicase selection has proven effective when a single task is broken down into many individual parameterizations. Evolved individuals have generalized across unique configurations of an overarching task. Here, we investigate the ability of Lexicase selection to generalize across multiple tasks, with each task again broken down into many instances. There are three objectives: to determine the feasibility of introducing additional tasks to the existing platform; to investigate any consequential effects of introducing these additional tasks during evolutionary adaptation; and to explore whether the schedule of presentation of the additional tasks over evolutionary time affects the final outcome. To address these aims we use a quadruped animat controlled by a feed-forward neural network with joint-angle, bearing-to-target, and spontaneous sinusoidal inputs. Weights in this network are trained using evolution with Lexicase-based parent selection. Simultaneous adaptation in a wall crossing task (labelled wall-cross) is explored when one of two different alternative tasks is also present: turn-and-seek or cargo-carry. Each task is parameterized into 100 distinct variants, and these variants are used as environments for evaluation and selection with Lexicase. We use performance in a single-task wall-cross environment as a baseline against which to examine the multi-task configurations. In addition, the objective sampling strategy (the manner in which tasks are presented over evolutionary time) is varied, and so data for treatments implementing uniform sampling, even sampling, or degrees of generational sampling are also presented. The Lexicase mechanism successfully integrates evolution of both turn-and-seek and cargo-carry with wall-cross, though there is a performance penalty compared to single task evolution. The size of the penalty depends on the similarity of the tasks. Complementary tasks (wallcross/turn-and-seek) show better performance than antagonistic tasks (wall-cross/cargo-carry). In complementary tasks performance is not affected by the sampling strategy. Where tasks are antagonistic, uniform and even sampling strategies yield significantly better performance than generational sampling. In all cases the generational sampling requires more evaluations and consequently more computational resources. The results indicate that Lexicase is a viable mechanism for multitask evolution of animat neurocontrollers, though the degree of interference between tasks is a key consideration. The results also support the conclusion that the naive, uniform random sampling strategy is the best choice when considering final task performance, simplicity of implementation, and computational efficiency

Lamarckian Evolution of Simulated Modular Robots

We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies

A Comparative Analysis of Darwinian Asexual and Sexual Reproduction in Evolutionary Robotics

Evolutionary Robotics systems draw inspiration from natural evolution to solve the problem of robot design. A key moment in the evolutionary process is reproduction, when the genotype of one or more parents is inherited by their offspring. Existent approaches have used both sexual and asexual reproduction but a comparison between the two is still missing. In this work, we study the effects of sexual and asexual reproduction on the controllers of an Evolutionary Robotics system. In our system, both morphologies and controllers are jointly evolved to solve two separate tasks. We adopt the Triangle of Life framework, in which the controllers go through a phase of learning before reproduction. Using extensive simulations we show that sexual reproduction of the robots' brains is preferable over asexual reproduction as it obtains better robots in terms of fitness. Moreover, we show that sexually reproducing robots present different morphologies and behaviors than the asexually reproducing ones, even though the reproduction mechanism only affects their brains. Finally, we study the effects of the reproduction mechanism on the robots' learning capabilities. By measuring the difference between the inherited and the learned brain we find that robots that evolved using sexual reproduction have better inherited brains and are also better learners

Memetic Robot Control Evolution and Adaption to Reality

Inspired by animals’ ability to learn and adapt to changes in their environment during life, hybrid evolutionary algorithms have been developed and successfully applied in a number of research areas. This paper explores the effects of learning combined with a genetic algorithm to evolve control system parameters for a four-legged robot. Here, learning corresponds to the application of a local search algorithm on individuals during evolution. Two types of learning were implemented and tested, i.e. Baldwinian and Lamarckian learning. On the direct results from evolution in simulation, Lamarckian learning showed promising results, with a significant increase in final fitness compared with the results from evolution without learning. Further experiments with learning on the real robot demonstrated an efficient adaptation of the robot gait to the real world environment, and increased the performance to the level measured in simulation. This paper demonstrates that Lamarckian evolution is effective in improving the performance of robot controller evolution, and that the same learning process on the physical robot efficiently reduces the negative impact of the simulation-reality gap. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works