Domain Randomization and Generative Models for Robotic Grasping

Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution over possible grasps. This model allows us to sample grasps efficiently at test time (or avoid sampling entirely). We evaluate our model architecture and data generation pipeline in simulation and the real world. We find we can achieve a $>$90% success rate on previously unseen realistic objects at test time in simulation despite having only been trained on random objects. We also demonstrate an 80% success rate on real-world grasp attempts despite having only been trained on random simulated objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018

Flexible couplings: diffusing neuromodulators and adaptive robotics

Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions¿here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one ¿chemical¿ and one ¿electrical.

Stochastic Ontogenesis in Evolutionary Robotics

This paper investigates the hypothesis that noise in the genotype–phenotype mapping, here called stochastic ontogenesis (SO), is an important consideration in Evolutionary Robotics. This is examined in two ways: first, in the context of seeking to generalise controller performance in an incremental task domain in simulation, and second, in a preliminary study of its effectiveness as a mechanism for crossing the “reality gap” from simulation to physical robots. The performance of evolved neurocontrollers for a fixed-morphology simulated robot is evaluated in both the presence and absence of ontogenic noise, in a task requiring the development of a walking gait that accommodates a varying environment. When SO is applied, evolution of controllers is more effective (replicates achieve higher fitness) and more robust (fewer replicates fail) than evolution using a deterministic mapping. This result is found in a variety of incremental scenarios. For the preliminary study of the utility of SO for moving between simulation and reality, the capacity of evolved controllers to handle unforeseen environmental noise is tested by introducing a stochastic coefficient of friction and evaluating previous populations in the new problem domain. Controllers evolved with deterministic ontogenesis fail to accommodate the new source of noise and show reduced fitness. In contrast, those which experienced ontogenic noise during evolution are not significantly disrupted by the additional noise in the environment. It is argued that SO is a catch-all mechanism for increasing performance of Evolutionary Robotics designs and may have further more general implications for Evolutionary Computation

Stochastic Ontogenesis in Evolutionary Robotics

This paper investigates the hypothesis that noise in the genotype–phenotype mapping, here called stochastic ontogenesis (SO), is an important consideration in Evolutionary Robotics. This is examined in two ways: first, in the context of seeking to generalise controller performance in an incremental task domain in simulation, and second, in a preliminary study of its effectiveness as a mechanism for crossing the “reality gap” from simulation to physical robots. The performance of evolved neurocontrollers for a fixed-morphology simulated robot is evaluated in both the presence and absence of ontogenic noise, in a task requiring the development of a walking gait that accommodates a varying environment. When SO is applied, evolution of controllers is more effective (replicates achieve higher fitness) and more robust (fewer replicates fail) than evolution using a deterministic mapping. This result is found in a variety of incremental scenarios. For the preliminary study of the utility of SO for moving between simulation and reality, the capacity of evolved controllers to handle unforeseen environmental noise is tested by introducing a stochastic coefficient of friction and evaluating previous populations in the new problem domain. Controllers evolved with deterministic ontogenesis fail to accommodate the new source of noise and show reduced fitness. In contrast, those which experienced ontogenic noise during evolution are not significantly disrupted by the additional noise in the environment. It is argued that SO is a catch-all mechanism for increasing performance of Evolutionary Robotics designs and may have further more general implications for Evolutionary Computation

A Visualization Tool for the Mini-Robot Khepera: Behaviour Analysis and Optimization

Löffler A, Klahold J, Hußmann M, Rückert U. A Visualization Tool for the Mini-Robot Khepera: Behaviour Analysis and Optimization. In: Floreano D, Nicoud J-D, Mondada F, eds. Proceedings of the 5th International European Conference on Artificial Life (ECAL99). Vol 1674. Lausanne, Switzerland: Springer-Verlag; 1999: 329-333.The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simulator and the experiment on a real robot with a thorough analysis. The available simulator tools in general only allow behavioral snapshots and do not provide the option of online interference. In order to cure these shortcomings, a visualization tool for aposteriori graphical analysis of recorded data sets which gives access to all relevant internal states and parameters of the system is presented. The mini-robot Khepera has been chosen as experimentatory platform