基于学习型地形与机器人感知动力学模型的上下文条件导航
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model
AI总结：
针对自主导航中地形属性与机器人动力学变化的适配问题，提出基于神经过程元学习的概率性地形与机器人感知前向动力学模型TRADYN，在模拟场景中实现了更优的长时域轨迹预测与导航规划性能。
摘要：
在自主导航场景中，多个物理量可能发生变化。摩擦系数等地形属性会随机器人所处位置的不同而随时间变化。此外，机器人的动力学特性也可能发生改变，例如不同的有效载荷会改变系统质量，或者磨损损耗会改变执行器增益或关节摩擦。因此，自主智能体应当具备适应此类变化的能力。本文提出了一种新型概率性、兼具地形与机器人感知能力的前向动力学模型，命名为TRADYN，能够适应上述各类变化。该模型基于神经过程（Neural Processes）的元学习前向动力学模型的最新进展构建。我们在模拟二维导航场景中评估了该方法，实验采用单轮式机器人，设置了具有空间变化摩擦系数的不同地形布局。实验结果表明，与非自适应的消融模型相比，所提出的模型在长时域轨迹预测任务中的预测误差更低。我们还在导航规划这一下游任务上评估了该模型，结果显示，通过将机器人与地形属性纳入考量，模型在规划控制效率更高的路径方面表现更优。
英文摘要：
In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.
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