4 accepted papers
Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a fr…
This paper proposes Retrieval-Enhanced Asymmetric Diffusion (READ) for image-based robot motion planning. Given an image of the scene READ retrieves an initial motion from a database of image-motion pairs and uses a diffusion model to refine the motion for the given scene. Unlike prior retrieval-bas…
Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this work, we explore the feasibility of plans produced by LfD.…
This paper proposes a probabilistic motion prediction method for long motions. The motion is predicted so that it accomplishes a task from the initial state observed in the given image. While our method evaluates the task achievability by the Energy-Based Model (EBM), previous EBMs are not designed…