The challenge of navigation in environments with dynamic objects continues to
be a central issue in the study of autonomous agents. While predictive methods
hold promise, their reliance on precise state information makes them less
practical for real-world implementation. This study presents visual forecasting
as an innovative alternative. By introducing intuitive visual cues, this
approach projects the future trajectories of dynamic objects to improve agent
perception and enable anticipatory actions. Our research explores two distinct
strategies for conveying predictive information through visual forecasting: (1)
sequences of bounding boxes, and (2) augmented paths. To validate the proposed
visual forecasting strategies, we initiate evaluations in simulated
environments using the Unity engine and then extend these evaluations to
real-world scenarios to assess both practicality and effectiveness. The results
confirm the viability of visual forecasting as a promising solution for
navigation and obstacle avoidance in dynamic environments.

该研究介绍了一种创新的可视化预测方法，通过引入直观的视觉线索，将动态物体的未来轨迹投影出来，以改善智能体的感知能力并实现预测性行动，验证了该方法在动态环境中的可行性和有效性。

视觉预测作为避让的中层表示

Visual Forecasting as a Mid-level Representation for Avoidance

We introduce a general framework for visual forecasting, which directly
imitates visual sequences without additional supervision. As a result, our
model can be applied at several semantic levels and does not require any domain
knowledge or handcrafted features. We achieve this by formulating visual
forecasting as an inverse reinforcement learning (IRL) problem, and directly
imitate the dynamics in natural sequences from their raw pixel values. The key
challenge is the high-dimensional and continuous state-action space that
prohibits the application of previous IRL algorithms. We address this
computational bottleneck by extending recent progress in model-free imitation
with trainable deep feature representations, which (1) bypasses the exhaustive
state-action pair visits in dynamic programming by using a dual formulation and
(2) avoids explicit state sampling at gradient computation using a deep feature
reparametrization. This allows us to apply IRL at scale and directly imitate
the dynamics in high-dimensional continuous visual sequences from the raw pixel
values. We evaluate our approach at three different level-of-abstraction, from
low level pixels to higher level semantics: future frame generation, action
anticipation, visual story forecasting. At all levels, our approach outperforms
existing methods.

我们引入了一种通用的视觉预测框架，旨在直接模仿视觉序列而不需要额外的监督。我们通过将视觉预测转化为逆强化学习问题，并通过可训练的深层特征表示扩展最近的进展来解决计算瓶颈。我们在低级像素到高级语义的三个不同级别上评估了我们的方法，在所有级别上，我们的方法都优于现有的方法。