Human cognition can leverage fundamental conceptual knowledge, like geometry and kinematic ones, to appropriately perceive, comprehend and interact with novel objects. Motivated by this finding, we aim to endow machine intelligence with an analogous capability through performing at the conceptual level, in order to understand and then interact with articulated objects, especially for those in novel categories, which is challenging due to the intricate geometric structures and diverse joint types of articulated objects. To achieve this goal, we propose Analytic Ontology Template (AOT), a parameterized and differentiable program description of generalized conceptual ontologies. A baseline approach called AOTNet driven by AOTs is designed accordingly to equip intelligent agents with these generalized concepts, and then empower the agents to effectively discover the conceptual knowledge on the structure and affordance of articulated objects. The AOT-driven approach yields benefits in three key perspectives: i) enabling concept-level understanding of articulated objects without relying on any real training data, ii) providing analytic structure information, and iii) introducing rich affordance information indicating proper ways of interaction. We conduct exhaustive experiments and the results demonstrate the superiority of our approach in understanding and then interacting with articulated objects.

Human trajectory prediction is fundamental for autonomous driving and service robot. The research community has studied various important aspects of this task and made remarkable progress recently. However, there is an essential perspective which is not well exploited in previous research all along, namely individual feedback. Individual feedback exists in the sequential nature of trajectory prediction, where earlier predictions of a target can be verified over time by his ground-truth trajectories to obtain feedback which provides valuable experience for subsequent predictions on the same agent. In this paper, we show such feedback can reveal the strengths and weaknesses of the model's predictions on a specific target and heuristically guide to deliver better predictions on him. We present an interactive adjustment network to effectively model and leverage the feedback. This network first exploits the feedback from previous predictions to dynamically generate an adjuster which then interactively makes appropriate adjustments to current predictions for more accurate ones. We raise a novel displacement expectation loss to train this interactive architecture. Through experiments on representative prediction methods and widely-used benchmarks, we demonstrate the great value of individual feedback and the superior effectiveness of proposed interactive adjustment network.

To comprehensively cover the uncertainty of the future, the common practice of multi-modal human trajectory prediction is to first generate a set/distribution of candidate future trajectories and then sample required numbers of trajectories from them as final predictions. Even though a large number of previous researches develop various strong models to predict candidate trajectories, how to effectively sample the final ones has not received much attention yet. In this paper, we propose stimulus verification, serving as a universal and effective sampling process to improve the multi-modal prediction capability, where stimulus refers to the factor in the observation that may affect the future movements such as social interaction and scene context. Stimulus verification introduces a probabilistic model, denoted as stimulus verifier, to verify the coherence between a predicted future trajectory and its corresponding stimulus. By highlighting prediction samples with better stimulus-coherence, stimulus verification ensures sampled trajectories plausible from the stimulus' point of view and therefore aids in better multi-modal prediction performance. We implement stimulus verification on five representative prediction frameworks and conduct exhaustive experiments on three widely-used benchmarks. Superior results demonstrate the effectiveness of our approach.

Predicting future trajectories of dynamic agents is inherently riddled with uncertainty. Given a certain historical observation, there are multiple plausible future movements people can perform. Notably, these possible movements are usually centralized around a few representative motion patterns, e.g. acceleration, deceleration, turning, etc. In this paper, we propose a novel prediction scheme which explores human behavior modality representations from real-world trajectory data to discover such motion patterns and further uses them to aid in trajectory prediction. To explore potential behavior modalities, we introduce a deep feature clustering process on trajectory features and each cluster can represent a type of modality. Intuitively, each modality is naturally a class, and a classification network can be adopted to retrieve highly probable modalities about to happen in the future according to historical observations. On account of a wide variety of cues existing in the observation (e.g. agents' motion states, semantics of the scene, etc.), we further design a gated aggregation module to fuse different types of cues into a unified feature. Finally, an adaptation process is proposed to adapt a certain modality to specific historical observations and generate fine-grained prediction results. Extensive experiments on four widely-used benchmarks show the superiority of our proposed approach.

Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as tracking losses and sudden events. However, the problem of trajectory pre-diction with limited observations has not drawn much at-tention in previous work. In this paper, we study a task named momentary trajectory prediction, which reduces the observed history from a long time sequence to an extreme situation of two frames, one frame for social and scene contexts and both frames for the velocity of agents. We perform a rigorous study of existing state-of-the-art approaches in this challenging setting on two widely used benchmarks. We further propose a unified feature extractor, along with a novel pre-training mechanism, to capture effective infor-mation within the momentary observation. Our extractor can be adopted in existing prediction models and substan-tially boost their performance of momentary trajectory pre-diction. We hope our work will pave the way for more re-sponsive, precise and robust prediction approaches, an important step toward real-world autonomous systems.