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.

翻译：在自主导航场景中，多个物理量可能发生变化。地形属性（如摩擦系数）会随机器人所处位置而时变，而机器人动力学特性也可能因载荷差异（改变系统质量）或机械磨损（改变执行器增益或关节摩擦）产生变化。因此，自主智能体需具备适应此类变化的能力。本文提出一种新型概率化地形与机器人感知前向动力学模型TRADYN，该模型能够适应上述变化，其构建基于神经过程元学习前向动力学模型的最新进展。我们在具备空间变化摩擦系数的不同地形布局中，以单轮式机器人仿真二维导航环境评估所提方法。实验表明，在长时域轨迹预测任务中，所提模型相比非自适应消融模型具有更低的预测误差。我们还将该模型应用于导航规划下游任务，验证了其通过融合机器人与地形特性可规划出更具控制效率的路径。