Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs
Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by aggregating various object states along the time; such a learned representation is accumulated as the robot's knowledge. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over pg-s, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectObject TrackingSimilar Papers 제목 키워드 기반
Real-time Holistic Robot Pose Estimation with Unknown States
Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. g…
6D Pose Estimation using RGBRobot Pose EstimationvalidEnhancing Joint Human-AI Inference in Robot Missions: A Confidence-Based Approach
Joint human-AI inference holds immense potential to improve outcomes in human-supervised robot missions. Current day missions are generally in the AI-assisted setting, where the human operator makes the final inference b…
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrep…
Bayesian InferenceExplainable Artificial Intelligence (XAI)Explanation GenerationDual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model
Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predicting states and actions due to the modality gap. To address this, we propos…
Transfer LearningSelf-supervised Learning Of Visual Pose Estimation Without Pose Labels By Classifying LED States
We introduce a model for monocular RGB relative pose estimation of a ground robot that trains from scratch without pose labels nor prior knowledge about the robot's shape or appearance. At training time, we assume: (i) a…
Monocular Depth EstimationSelf-Supervised LearningRobot Pose Estimation