GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data

Embodied foundation models have achieved significant breakthroughs in robotic manipulation, yet they still depend heavily on large-scale robot demonstrations. Although recent works have explored leveraging human data to alleviate this dependency, effectively extracting transferable knowledge remains a significant challenge due to the inherent embodiment gap between human and robot. We argue that the intention underlying human actions can serve as a powerful intermediate representation for bridging this gap. In this paper, we introduce a novel framework that explicitly learns and transfers human intention to facilitate robotic manipulation. Specifically, we model intention through gaze, as it naturally precedes physical actions and serves as an observable proxy for human intent. Our model is first pretrained on a large-scale egocentric human dataset to capture human intention and its synergy with action, followed by finetuning on a small set of robot and human data. During inference, the model adopts a Chain-of-Thought reasoning paradigm, sequentially predicting intention before executing the action. Extensive evaluations in simulation and real-world settings, across long-horizon and fine-grained tasks, and under few-shot and robustness benchmarks, show that our method consistently outperforms strong baselines, generalizes better, and achieves state-of-the-art performance. Project page: https://gazevla.github.io .

Action segmentation supports intention estimation and execution monitoring in assisted teleoperation, but robot demonstrations are costly to collect. We propose an uncertainty-aware framework that transfers temporal knowledge from human to robot demonstrations through MS-TCN++~\cite{li2020mstcnpp} pretraining and fine-tuning, reliability-aware sequence correction, and class-conditional conformal prediction. Across 22 toy-car assembly action classes, fine-tuning with 9 robot demonstrations improves test Edit score from 55.23 to 72.51 and segmental F1@50 from 10.97 to 16.86. The reliability analyzer identifies incorrect segments with 0.755 AUROC. Unrestricted correction reduces Edit to 67.86, while selective acceptance maintains 73.45, primarily safeguarding against harmful relabeling. Conformal prediction achieves 95.38% empirical coverage at a 93% target, but prediction sets average 17.41 of 22 classes. These findings support human temporal pretraining while revealing persistent challenges in reliable correction and informative uncertainty quantification with limited robot supervision.

Robotic manipulation systems commonly learn object affordances from human-annotated datasets, which reflect interaction possibilities for a human hand rather than the physical constraints of a specific robot. For example, a surface region of an object identified as graspable by a human annotator may be geometrically unreachable or mechanically infeasible for a robot, yet this mismatch is rarely addressed explicitly. To tackle this challenge, we proposed a framework that translates human affordance annotations into robot-executable affordance predictions conditioned on the physical properties of the robot. The network predicts which surface regions of an object are physically executable by the robot, given a 3D point cloud of an object and a descriptor encoding the robot's geometry and constraints. The model was trained using human-annotated affordances together with robotic execution outcomes from multiple sampled approach trials in simulation, where execution outcomes determined which regions are executable for the robot. The evaluation was performed on a household objects dataset using two parallel-jaw gripper configurations on a UR5e robot arm. The framework outperformed a model that does not receive information about the robot. The experimental results demonstrated that affordance prediction must be conditioned on the robot and that human annotations alone are insufficient for learning robot-executable affordance representations.