# Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints

arXiv: https://arxiv.org/abs/2507.10131 · HTML: https://mlsanity.com/paper/2507.10131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Learning Dexterous Grasping with Object-Centric Visual Affordances](https://mlsanity.com/paper/2009.01439/) (2020)
- [Human Trajectory Prediction via Neural Social Physics](https://mlsanity.com/paper/2207.10435/) (2022)
- [IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality](https://mlsanity.com/paper/2305.17110/) (2023)
- [Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming](https://mlsanity.com/paper/1706.00155/) (2017)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Spatial Intention Maps for Multi-Agent Mobile Manipulation](https://mlsanity.com/paper/2103.12710/) (2021)
- [Predictive Runtime Monitoring for Mobile Robots using Logic-Based Bayesian Intent Inference](https://mlsanity.com/paper/2108.01227/) (2021)
- [Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation](https://mlsanity.com/paper/2402.02499/) (2024)
- [Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots](https://mlsanity.com/paper/2501.02902/) (2025)
- [Active Intent Disambiguation for Shared Control Robots](https://mlsanity.com/paper/2005.03652/) (2020)
- [A Bayesian-Based Approach to Human Operator Intent Recognition in Remote Mobile Robot Navigation](https://mlsanity.com/paper/2109.12045/) (2021)
- [SpectGRASP: Robotic Grasping by Spectral Correlation](https://mlsanity.com/paper/2107.12492/) (2021)
- [Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym](https://mlsanity.com/paper/2403.07091/) (2024)
- [Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation](https://mlsanity.com/paper/2409.00215/) (2024)
- [A System for Traded Control Teleoperation of Manipulation Tasks using Intent Prediction from Hand Gestures](https://mlsanity.com/paper/2107.01829/) (2021)
- [Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise](https://mlsanity.com/paper/2207.00648/) (2022)
- [A Mini-Review on Mobile Manipulators with Variable Autonomy](https://mlsanity.com/paper/2408.10887/) (2024)
- [Semi-autonomous Robotic Disassembly Enhanced by Mixed Reality](https://mlsanity.com/paper/2405.03530/) (2024)

## Built on by (most cited first, 2 of 2)

- [Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance](https://mlsanity.com/paper/2508.11093/) (2025)
- [Intent-Driven LLM Ensemble Planning for Flexible Multi-Robot Disassembly: Demonstration on EV Batteries](https://mlsanity.com/paper/2510.17576/) (2025)

## Abstract (arXiv)

Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots. We present GUIDER (Global User Intent Dual-phase Estimation for Robots), a probabilistic framework that enables a robot to estimate the intent of human operators. GUIDER maintains two coupled belief layers, one tracking navigation goals and the other manipulation goals. In the Navigation phase, a Synergy Map blends controller velocity with an occupancy grid to rank interaction areas. Upon arrival at a goal, an autonomous multi-view scan builds a local 3D cloud. The Manipulation phase combines U2Net saliency, FastSAM instance saliency, and three geometric grasp-feasibility tests, with an end-effector kinematics-aware update rule that evolves object probabilities in real-time. GUIDER can recognize areas and objects of intent without predefined goals. We evaluated GUIDER on 25 trials (five participants x five task variants) in Isaac Sim, and compared it with two baselines, one for navigation and one for manipulation. Across the 25 trials, GUIDER achieved a median stability of 93-100% during navigation, compared with 60-100% for the BOIR baseline, with an improvement of 39.5% in a redirection scenario (T5). During manipulation, stability reached 94-100% (versus 69-100% for Trajectron), with a 31.4% difference in a redirection task (T3). In geometry-constrained trials (manipulation), GUIDER recognized the object intent three times earlier than Trajectron (median remaining time to confident prediction 23.6 s vs 7.8 s). These results validate our dual-phase framework and show improvements in intent inference in both phases of mobile manipulation tasks.