  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.
