How can a robot navigate successfully in a rich and diverse environment, indoors or outdoors, along an office corridor or a trail in the park, on the flat ground, the staircase, or the elevator, etc.? To this end, this work aims at three challenges: (i) complex visual observations, (ii) partial observability of local sensing, and (iii) multimodal navigation behaviors that depend on both the local environment and the high-level goal. We propose a novel neural network (NN) architecture to represent a local controller and leverage the flexibility of the end-to-end approach to learn a powerful policy. To tackle complex visual observations, we extract multiscale spatial information through convolution layers. To deal with partial observability, we encode rich history information in LSTM-like modules. Importantly, we integrate the two into a single unified architecture that exploits convolutional memory cells to track the observation history at multiple spatial scales, which can capture the complex spatiotemporal dependencies between observations and controls. We additionally condition the network on the high-level goal in order to generate different navigation behavior modes. Specifically, we propose to use independent memory cells for different modes to prevent mode collapse in the learned policy. We implemented the NN controller on the SPOT robot and evaluate it on three challenging tasks with partial observations: adversarial pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. Our model significantly outperforms CNNs, conventional LSTMs, or the ablated versions of our model. A demo video will be publicly available, showing our SPOT robot traversing many different locations on our university campus.
翻译:机器人如何在丰富多样的环境中、室内或室外、办公室走廊或公园的轨迹、平地、楼梯或电梯等成功导航?为此,这项工作旨在应对三个挑战:(一) 复杂的视觉观测,(二) 局部可视性,(三) 取决于当地环境和高层目标的多式导航行为。我们提议了一个新型神经网络架构,代表当地控制者,利用端对端方法的灵活性学习强有力的政策。为了应对复杂的视觉观察,我们通过交错层提取多层空间信息。为了应对部分易感性,我们把丰富的历史信息编码在LSTM类模块中。重要的是,我们将这两部数据整合到一个单一的统一架构中,利用革命记忆细胞来跟踪多层观测历史,从而捕捉观察和控制之间复杂的模型时空依赖性。我们在高端目标上还设置了灵活性,以预防不同的导航行为模式。具体地说,我们建议使用独立的智能模型来展示我们的智能机路段。