Visual Proactivity: Enhancing Human-Robot Collaboration Through Intent Communication

Valerio Bo; Edison Bejarano; Anaís Garrell; Alberto Sanfeliu

cs.ROSep 18, 2026

Visual Proactivity: Enhancing Human-Robot Collaboration Through Intent Communication

Authors: Valerio Bo, Edison Bejarano, Anaís Garrell, Alberto Sanfeliu

Organizations: Institut de Robotica y Informatica Industrial (CSIC-UPC) and Universitat Politecnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain

Abstract

As robots transition from performing repetitive tasks to collaborating with humans, understanding human intent becomes crucial to effective interaction. Anticipation enables robots to predict human actions, while proactivity allows them to take initiative and guide human behavior toward optimal outcomes. Although research has largely focused on how robots infer and respond to human intentions, less attention has been paid to how robots communicate their own intent. This paper introduces visual proactivity, a novel, simple yet effective approach that enables robots to communicate their intentions through visual feedback, influencing human behavior and enhancing transparency and fluency. We develop and evaluate proactive robotic behaviors in a human-to-robot handover scenario, where a user study validates human perception of reactive, anticipatory, and proactive behaviors. The results demonstrate that effective visual proactivity fosters better alignment and coordination, paving the way for more intuitive human-robot collaboration.

Figures & tables

Fig. 1: Visual proactive behavior. Example in a human-to-robot handover task. The human decides to go to a specific goal (goal 4) but seeing the robot is moving towards another, the human changes his goal (goal 3).

Panel of Figure 2 — Fig. 2: Schematic of reactive, anticipatory, and proactive approaches. In all the figures, the approaching direction of the robot $d_{f}$ is represented in red. In (c), the direction between the robot and the human is depicted with dashed black, the direction between the robot and the closest goal in green, and the direction between the robot and the prediction of the human in blue.

Fig. 3: Experimental setup. Four goals (numbered circles) are depicted considering the presence of three obstacles (squares). In the bottom left corner, a camera is positioned for recording. Note that volunteers were free to move as they preferred, without following precise, definite routes.

Panel of Figure 4 — Fig. 4: Experimental trials. Examples of (a) reactive, (b) anticipatory, and (c) proactive approaches. In (c), the human changes his course, without the need for the robot to ask to choose between goals 3 and 4.

Panel of Figure 5 — Fig. 5: Bar chart for H1 and H2. Results from the questions considered for (a) H1 and (b) H2. The median and interquartile range for the three behaviors are plotted. p-values are reported above the bar charts. The symbols $*$ , $**$ and $***$ denote the levels of statistical significance.

Panel of Figure 6 — Fig. 6: Human trajectory clusters. Obtained clusters from the human trajectories for Goal 2 and Goal 3 with GMM, where $\mu$ and $\sigma$ represent the mean and the standard deviation of the distribution, respectively. Two different clusters are evident where the volunteer tends to go to a different goal, but in the end, chooses the goal where the robot is moving.

Panel of Figure 7 — Fig. 7: Bar chart for H3 and H4 . Results from the questions considered for (a) H3 and (b) H4. The median and interquartile range are reported for all behaviors. p-values are reported above the bar charts.

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Sep 21, 2026J/K move · Enter open · S save

Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport

Elvin Yang, Christoforos Mavrogiannis

Department of Robotics, University of Michigan, Ann Arbor, United States

Robotic Control Human-Robot Collaboration Human Behavior Prediction

Open paper AskPDF arXiv

We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2% and 20.4%, and mean completion time by 12.9% and 6.9%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at https://youtu.be/qAGvQfVPjbk.

Abstract

Explore similar work

Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport

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