# Gentle Object Retraction in Dense Clutter Using Multimodal Force Sensing and Imitation Learning

arXiv: https://arxiv.org/abs/2508.19476 · HTML: https://mlsanity.com/paper/2508.19476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 30 of 32)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Causal Confusion in Imitation Learning](https://mlsanity.com/paper/1905.11979/) (2019)
- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [Learning Visuotactile Skills with Two Multifingered Hands](https://mlsanity.com/paper/2404.16823/) (2024)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation](https://mlsanity.com/paper/2410.07554/) (2024)
- [DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2501.10356/) (2025)
- [ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface](https://mlsanity.com/paper/2504.06156/) (2025)
- [Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter](https://mlsanity.com/paper/2008.06073/) (2020)
- [Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement](https://mlsanity.com/paper/2105.02857/) (2021)
- [PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies](https://mlsanity.com/paper/2504.19341/) (2025)
- [Occlusion-Aware Search for Object Retrieval in Clutter](https://mlsanity.com/paper/2011.03334/) (2020)
- [Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor](https://mlsanity.com/paper/2311.01248/) (2023)
- [Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter](https://mlsanity.com/paper/2202.02207/) (2022)
- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://mlsanity.com/paper/2209.07682/) (2022)
- [Mechanical Search on Shelves using Lateral Access X-RAY](https://mlsanity.com/paper/2011.11696/) (2020)
- [Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval](https://mlsanity.com/paper/2201.10434/) (2022)
- [TactoFind: A Tactile Only System for Object Retrieval](https://mlsanity.com/paper/2303.13482/) (2023)
- [KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation](https://mlsanity.com/paper/2505.01974/) (2025)
- [COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learning](https://mlsanity.com/paper/2508.01131/) (2025)
- [Manipulation in Clutter with Whole-Arm Tactile Sensing](https://mlsanity.com/paper/1304.6146/) (2013)
- [Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments](https://mlsanity.com/paper/1711.09604/) (2017)
- [Real-Time Online Re-Planning for Grasping Under Clutter and Uncertainty](https://mlsanity.com/paper/1807.09049/) (2018)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration](https://mlsanity.com/paper/2105.02345/) (2021)
- [Going In Blind: Object Motion Classification using Distributed Tactile Sensing for Safe Reaching in Clutter](https://mlsanity.com/paper/2210.00137/) (2022)
- [Robot Safe Planning In Dynamic Environments Based On Model Predictive Control Using Control Barrier Function](https://mlsanity.com/paper/2404.05952/) (2024)

## Built on by (most cited first, 1 of 1)

- [Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback](https://mlsanity.com/paper/2606.14218/) (2026)

## Abstract (arXiv)

Dense collections of movable objects are common in everyday spaces-from cabinets in a home to shelves in a warehouse. Safely retracting objects from such collections is difficult for robots, yet people do it frequently, leveraging learned experience in tandem with vision and non-prehensile tactile sensing on the sides and backs of their hands and arms. We investigate the role of contact force sensing for training robots to gently reach into constrained clutter and extract objects. The available sensing modalities are (1) "eye-in-hand" vision, (2) proprioception, (3) non-prehensile triaxial tactile sensing, (4) contact wrenches estimated from joint torques, and (5) a measure of object acquisition obtained by monitoring the vacuum line of a suction cup. We use imitation learning to train policies from a set of demonstrations on randomly generated scenes, then conduct an ablation study of wrench and tactile information. We evaluate each policy's performance across 40 unseen environment configurations. Policies employing any force sensing show fewer excessive force failures, an increased overall success rate, and faster completion times. The best performance is achieved using both tactile and wrench information, producing an 80% improvement above the baseline without force information.