Therefore, to transfer a policy on the source robot to multiple target robots, they must launch multiple independent runs for each target robot. We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method named Meta-Evolve that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2 and one-to-six transfer of agile locomotion policy by 2.4 in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers. The robotics industry has designed and developed a large number of commercial robots deployed in various applications. How to efficiently learn robotic skills on diverse robots in a scalable fashion? A popular solution is to train a policy for every new robot on every new task from scratch. This is not only inefficient in terms of sample efficiency but also impractical for complex robots due to a large exploration space. Inter-robot imitation by statistic matching methods that optimize to match the distribution of actions (Ross et al., 2011), transitioned states (Liu et al., 2019; Radosavovic et al., 2020), or reward (Ng et al., 2000; Ho & Ermon, 2016) could be possible solutions. However, they can only be applied to robots with similar dynamics to yield optimal performance. Recent advances in evolution-based imitation learning (Liu et al., 2022a;b) inspire us to view this problem from the perspective of policy transferring from one robot to another. The core idea is to interpolate two different robots by producing a large number of intermediate robots between them which gradually evolve from the source robot toward the target robot.

The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.

LONDON – Inspired by his belief that human beings are essentially terrified of robots, Ben Russell set about charting the evolution of automatons for an exhibition he hopes will force people to think about how androids and other robotic forms can enhance their lives. Robots, says Russell, have been with us for centuries -- as "Robots," his exhibit opening Wednesday at London's Science Museum, shows. From a 15th century Spanish clockwork monk who kisses his rosary and beats his breast in contrition, to a Japanese "childoid" newsreader, created in 2014 with lifelike facial expressions, the exhibition tracks the development of robotics and mankind's obsession with replicating itself. Arnold Schwarzenegger's unstoppable Terminator cyborg is there, as is Robby the Robot, star of the 1956 film "Forbidden Planet," representing the horror and the fantasy of robots with minds of their own. There are also examples of factory production-line machines blamed for taking people's jobs in recent decades; a "telenoid communications android" for hugging during long-distance phone calls to ease loneliness; and Kaspar, a "minimally expressive social robot" built like a small boy and designed to help ease social interactions for children with autism.