Data-Efficient Learning from Demonstration for Humanoid Robots via Feature-Level Perception and Prompt-Based Object Detection

Learning from Demonstration (LfD) offers a promising paradigm for robot skill acquisition. Recent approaches attempt to extract manipulation commands directly from video demonstrations, yet face two critical challenges: (1) general video captioning models prioritize global scene features over task-relevant objects, producing descriptions unsuitable for precise robotic execution, and (2) end-to-end architectures coupling visual understanding with policy learning require extensive paired datasets and struggle to generalize across objects and scenarios. To address these limitations, we propose a novel ``Human-to-Robot'' imitation learning pipeline that enables robots to acquire manipulation skills directly from unstructured video demonstrations, inspired by the human ability to learn by watching and imitating. Our key innovation is a modular framework that decouples the learning process into two distinct stages: (1) Video Understanding, which combines Temporal Shift Modules (TSM) with Vision-Language Models (VLMs) to extract actions and identify interacted objects, and (2) Robot Imitation, which employs TD3-based deep reinforcement learning to execute the demonstrated manipulations. We validated our approach in PyBullet simulation environments with a UR5e manipulator and in a real-world experiment with a UF850 manipulator across four fundamental actions: reach, pick, move, and put. For video understanding, our method achieves 89.97% action classification accuracy and BLEU-4 scores of 0.351 on standard objects and 0.265 on novel objects, representing improvements of 76.4% and 128.4% over the best baseline, respectively. For robot manipulation, our framework achieves an average success rate of 87.5% across all actions, with 100% success on reaching tasks and up to 90% on complex pick-and-place operations. The project website is available at https://thanhnguyencanh.github.io/LfD4hri.

Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction. This paper presents a framework for learning human-like robot motion from demonstration, including data collection, probabilistic trajectory learning, and perceptual user evaluation. A dataset of 3,142 handwriting demonstrations was collected from 22 participants across all 52 Latin alphabet character-case combinations via a touchscreen teleoperation interface, capturing planar position, contact force, and timing. Building on the widely used Gaussian Mixture Model and Gaussian Mixture Regression approach for learning from demonstration, the framework is extended in this work by incorporating force and normalised time dimensions to enable richer representation of human dynamics, and adapting it to handle non-continuous, multi-segment trajectories, enabling generalisation across demonstrations. A user study with 21 participants evaluated the perceived human-likeness of the generated trajectories using a continuous scale anchored between robotic and human-like motion, normalised to 0-100 where 50 represents the neutral midpoint. The generated trajectories achieved an overall human-likeness score of 71.50 (SD=22.56), indicating that the majority of trajectories were perceived as more human-like. Participants identified geometric positioning and trajectory sequence as the most influential perceptual factors, and reported positive attitudes toward human-like robot behaviour. The datasets are released as open-source, providing a reproducible benchmark for developing and evaluating human-like robot motion methods.

High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception, locomotion, and manipulation. Existing data-collection methods largely rely on robot teleoperation, which is constrained by hardware accessibility, operator expertise, and limited efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose HumanoidUMI, a portable and robot-free framework for humanoid whole-body data collection. HumanoidUMI uses lightweight VR devices and UMI-inspired grippers to collect sparse human keypoint trajectories, wrist-view observations, and gripper actions. These demonstrations train a high-level policy to predict future keypoints, which are retargeted to robot-native whole-body references and executed by a whole-body controller. Experiments in five real-world scenarios demonstrate the effectiveness of the proposed framework and validate the collected demonstrations for transferable humanoid whole-body skill learning.