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，多个变量可能存在波动。地形特性（如摩擦系数）可能随时间及机器人位置变化而发生改变；同时，机器人动力学特性也可能因有效载荷差异（改变系统质量）、机械磨损（影响执行器增益或关节摩擦）等因素产生变化。自主智能体应具备适应此类变化的能力。本文提出一种新型概率性前向动力学模型TRADYN（地形与机器人感知动力学模型），该模型能够适应上述变化。该模型基于神经过程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，采用单轮类机器人及具有空间变化摩擦系数的不同地形布局进行实验评估。实验结果表明，相较于非自适应消融模型，所提模型在长时域轨迹预测任务中展现出更低的预测误差。此外，我们还将该模型应用于下游导航规划任务，验证了其通过整合机器人与地形特性规划出控制效率更优路径的性能提升效果。