A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboration. This framework fuses four heterogeneous modalities including object 6D pose, gaze, skeletal motion and IMU-based hand motion. It embeds a confidence-trend-driven dynamic fusion mechanism into BiLSTM to adaptively balance bidirectional temporal features according to real-time modality reliability. A confidence-guided balanced learning strategy combined with a confidence freezing mechanism is further adopted to adjust network gradients dynamically, suppress noise from low-quality modalities and mitigate cross-modal learning bias. A physical platform based on the UR3 collaborative robot is built for experimental validation. Comparative results show that the proposed method reaches an intention recognition accuracy of 91.86% and outperforms existing multimodal fusion approaches in overall performance and stability. It also maintains satisfactory accuracy under low light and partial occlusion interference. In practical assembly tasks, the framework enables proactive and stable human-robot cooperation with strong environmental adaptability.
Safe human-robot collaboration (HRC) requires accurate human pose estimation and motion prediction to prevent critical collisions. Existing certifiable safe HRC approaches are highly conservative or rely on marker-based motion tracking, while vision-based pose estimators lack the statistical guarantees required for certification in accordance with ISO 13849-1. Hence, we propose a pipeline that predicts 3D human motion and strong probabilistic bounds on the prediction error using conformal prediction. A gradient-based monitor detects out-of-distribution input poses and replaces them with poses from past predicted motions to maintain smooth operation. The resulting conformal prediction sets directly integrate into the provably safe HRC approach SARA shield. In experiments on the Human3.6M dataset and a real-world HRC setting, our conformal prediction sets have a 7.6 times smaller volume than model-based predictions, and we bound the probability of a dangerous failure per hour by 9.5E-7 with 99.999 % confidence under our test distribution, which is necessary but not sufficient for performance level d. All code and models are available at https://jakob-thumm.com/conformal_human_motion_prediction/.
In assisted teleoperation for human-robot collaboration, accurate intention prediction is critical for enabling timely and reliable robotic assistance during long-horizon manipulation and assembly tasks. These systems require continuous understanding of user behavior to recognize actions, anticipate intentions, and detect mistakes in real time. However, robot teleoperation demonstrations are costly and hardware-limited, whereas human demonstrations are easier to collect and provide rich temporal structure. To address this challenge, we propose an uncertainty-aware human-to-robot intention prediction framework that combines: (1) hierarchical transfer learning, where MS-TCN++ is pretrained on human hand demonstrations and fine-tuned on limited robot teleoperation data to capture low-level actions and high-level task intentions; (2) a conformal prediction module that provides frame-level prediction sets with statistical coverage guarantees for reliable uncertainty quantification and early intention estimation; and (3) VLM-guided segment correction, which selectively reviews low-confidence or temporally uncertain segments using visual and temporal context. The framework supports action recognition, temporal segmentation, intention anticipation, and mistake detection for assisted teleoperation. Experiments on robot assembly demonstrations with 22 action classes show that human-to-robot fine-tuning improves the robot test-set Edit score from 70.50 to 80.70 using only 16 robot demonstrations. Edit-safe VLM correction further improves frame accuracy from 45.21% to 46.42% and increases F1@25 and F1@50 while preserving the Edit score. These results show that human demonstrations provide scalable pretraining data for robust, uncertainty-aware robot action segmentation. Code and data: project website.
Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data. Attention-based models are appealing for processing multimodal data because they can handle multiple modalities with a single backbone network. However, most multimodal models assume that all modalities are available during both training and inference, limiting their applicability in robotic perception and decision-making. In this paper, we introduce a multimodal model designed to handle missing modalities during both training and inference. The model is formulated as a conditional variational autoencoder (CVAE) and incorporates a transformer-based architecture that leverages attention mechanisms to learn a unified, fixed-dimensional representation, even when some modalities are missing. We show that our proposed model can be trained with missing modalities while approximating a robust representation of all modalities. We evaluate our approach on five multimodal datasets across two robot learning tasks: human trajectory prediction and robot manipulation forecasting. Experimental results demonstrate that our model effectively learns from incomplete data and is superior to prior multimodal fusion approaches.