# Active-Perceptive Motion Generation for Mobile Manipulation

arXiv: https://arxiv.org/abs/2310.00433 · HTML: https://mlsanity.com/paper/2310.00433/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter](https://mlsanity.com/paper/2101.01132/) (2021)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2103.10534/) (2021)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [Closed-Loop Next-Best-View Planning for Target-Driven Grasping](https://mlsanity.com/paper/2207.10543/) (2022)
- [Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter](https://mlsanity.com/paper/1809.08564/) (2018)
- [Online Next-Best-View Planner for 3D-Exploration and Inspection With a Mobile Manipulator Robot](https://mlsanity.com/paper/2203.10113/) (2022)
- [Reuleaux: Robot Base Placement by Reachability Analysis](https://mlsanity.com/paper/1710.01328/) (2017)
- [Mobile Manipulation Leveraging Multiple Views](https://mlsanity.com/paper/2110.00717/) (2021)
- [Graph-based View Motion Planning for Fruit Detection](https://mlsanity.com/paper/2303.03048/) (2023)
- [Combining Local and Global Viewpoint Planning for Fruit Coverage](https://mlsanity.com/paper/2108.08114/) (2021)

## Built on by (most cited first, 10 of 10)

- [RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation](https://mlsanity.com/paper/2402.15487/) (2024)
- [Instruction-Augmented Long-Horizon Planning: Embedding Grounding Mechanisms in Embodied Mobile Manipulation](https://mlsanity.com/paper/2503.08084/) (2025)
- [Learning to Look: Seeking Information for Decision Making via Policy Factorization](https://mlsanity.com/paper/2410.18964/) (2024)
- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation](https://mlsanity.com/paper/2509.14980/) (2025)
- [QuadWBG: Generalizable Quadrupedal Whole-Body Grasping](https://mlsanity.com/paper/2411.06782/) (2024)
- [UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene](https://mlsanity.com/paper/2510.06754/) (2025)
- [Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers](https://mlsanity.com/paper/2604.12509/) (2026)
- [HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction](https://mlsanity.com/paper/2609.18548/) (2026)
- [INSPECT: Learning Robot View Selection from Assistant Use](https://mlsanity.com/paper/2609.20615/) (2026)

## Abstract (arXiv)

Mobile Manipulation (MoMa) systems incorporate the benefits of mobility and dexterity, due to the enlarged space in which they can move and interact with their environment. However, even when equipped with onboard sensors, e.g., an embodied camera, extracting task-relevant visual information in unstructured and cluttered environments, such as households, remains challenging. In this work, we introduce an active perception pipeline for mobile manipulators to generate motions that are informative toward manipulation tasks, such as grasping in unknown, cluttered scenes. Our proposed approach, ActPerMoMa, generates robot paths in a receding horizon fashion by sampling paths and computing path-wise utilities. These utilities trade-off maximizing the visual Information Gain (IG) for scene reconstruction and the task-oriented objective, e.g., grasp success, by maximizing grasp reachability. We show the efficacy of our method in simulated experiments with a dual-arm TIAGo++ MoMa robot performing mobile grasping in cluttered scenes with obstacles. We empirically analyze the contribution of various utilities and parameters, and compare against representative baselines both with and without active perception objectives. Finally, we demonstrate the transfer of our mobile grasping strategy to the real world, indicating a promising direction for active-perceptive MoMa.