Contact-Rich Robotic Manipulation

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Overview

This field studies how robots make and maintain useful contact with objects, where small errors in force, alignment, or friction can cause failure or damage. Approaches include tactile- and force-aware imitation learning and reinforcement learning, compliant and hybrid force-position control, contact-dynamics models, and planning methods that reason over contact modes. Recent work also brings these capabilities into vision-language-action policies and predictive world models, often using tactile feedback for fast corrections while vision guides the broader task. A central challenge is making policies safe and reliable across unseen objects, sensors, and real-world contact conditions, since more sensing or higher simulation fidelity does not consistently improve every task.

Latest papers

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420 papers78 papers in the last four weeks, down 4% on the four weeks before. 0.5% of all new papers.

May 6, 2026

From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances

High-precision assembly frequently involves tight-tolerance insertions, where even slight pose errors can cause jamming or excessive interaction forces, making robust and safe insertion policies difficult to obtain. This paper proposes a tactile-augmented two-stage method that combines Imitation Learning (IL) and Reinforcement Learning (RL) for precision insertion tasks. In the first stage, IL learns a reaching policy with position generalization that grasps the peg and brings it to the vicinity of the target region. In the second stage, RL executes the insertion and enables recovery from failures during contact-rich interactions. To better exploit tactile feedback, we introduce tactile group sampling to increase coverage of critical contact segments during training, and design a tactile critic to more accurately evaluate policy values, improving insertion performance while maintaining low contact forces. We conduct systematic experiments across five hole geometries and three clearance settings. Results show that our method substantially improves insertion performance across all settings; under the most challenging 0.05,mm clearance, it achieves a 67% success rate while keeping contact forces low, reducing the maximum interaction force by 60% and torque by 44%, thereby validating both effectiveness and safety for precision assembly.

Xinpan Meng, Siyao Huang, JingPu Yang +6

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

RL for RoboticsContact-Rich Robotic ManipulationRobotic InsertionTactile Sensing for Robotic Manipulation
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May 5, 2026

RLDX-1 Technical Report

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, long-term memory, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including data synthesis for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. π0.5π_{0.5} and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while π0.5π_{0.5} and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation.

Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65

Contact-Rich Robotic ManipulationRobotic ManipulationVision-Language-Action Models
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May 4, 2026

CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understanding, applying them directly to contact-rich manipulation remains a challenge due to their lack of explicit physical grounding and inability to perform adaptive control. To bridge this gap, we propose CoRAL (Contact-Rich Adaptive LLM-based control), a modular framework that enables zero-shot planning by decoupling high-level reasoning from low-level control. Unlike black-box policies, CoRAL uses LLMs not as direct controllers, but as cost designers that synthesize context-aware objective functions for a sampling-based motion planner (MPPI). To address the ambiguity of physical parameters in visual data, we introduce a neuro-symbolic adaptation loop: a VLM provides semantic priors for environmental dynamics, such as mass and friction estimates, which are then explicitly refined in real time via online system identification, while the LLM iteratively modulates the cost-function structure to correct strategic errors based on interaction feedback. Furthermore, a retrieval-based memory unit allows the system to reuse successful strategies across recurrent tasks. This hierarchical architecture ensures real-time control stability by decoupling high-level semantic reasoning from reactive execution, effectively bridging the gap between slow LLM inference and dynamic contact requirements. We validate CoRAL on both simulation and real-world hardware across challenging and novel tasks, such as flipping objects against walls by leveraging extrinsic contacts. Experiments demonstrate that CoRAL outperforms state-of-the-art VLA and foundation-model-based planner baselines by boosting success rates over 50% on average in unseen contact-rich scenarios, effectively handling sim-to-real gaps through its adaptive physical understanding.

Berk Çiçek, Mert K. Er, Ozgur S. Oguz

LiRA Lab, Department of Computer Engineering, Bilkent University, Ankara, Türkiye

Language Model-Based ControlLarge Language Model-Based Robot PlanningContact-Rich Robotic ManipulationVLMs for Robotics
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Apr 29, 2026

Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies

Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: vision and proprioception alone cannot resolve uncertain, evolving interactions with the environment. Tactile sensing offers direct contact observability, but scalable tactile-aware learning framework for quadrupedal loco-manipulation is still underexplored. In this paper, we present a tactile-aware loco-manipulation policy learning pipeline with a hierarchical structure. Our approach has two key components. First, we leverage real-world human demonstrations to train a tactile-conditioned visuotactile high-level policy. This policy predicts not only end-effector trajectories for manipulation, but also the evolving tactile interaction cues that characterize how contact should develop over time. Second, we perform large-scale reinforcement learning in simulation to learn a tactile-aware whole-body control policy that tracks diverse commanded trajectories and tactile interaction cues, and transfers zero-shot to the real world. Together, these components enable coordinated locomotion and manipulation under contact-rich scenarios. We evaluate the system on real-world contact-rich tasks, including in-hand reorientation with insertion, valve tightening, and delicate object manipulation. Compared to vision-only and visuotactile baselines, our method improves performance by 28.54% on average across these tasks.

Pokuang Zhou, Yuhao Zhou, Quan Khanh Luu +7

Purdue University, West Lafayette, IN, USA. · Istituto Italiano di Tecnologia, Genoa, Italy. · Columbia University, New York, NY, USA.

Legged RoboticsRL for RoboticsContact-Rich Robotic ManipulationTactile Sensing for Robotic ManipulationRobotic Loco-Manipulation
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Apr 29, 2026

Global Sampling-Based Trajectory Optimization for Contact-Rich Manipulation via KernelSOS

Contact-rich manipulation is challenging due to its high dimensionality, the requirement for long time horizons, and the presence of hybrid contact dynamics. Sampling-based methods have become a popular approach for this class of problems, but without explicit mechanisms for global exploration, they are susceptible to converging to poor local minima. In this paper, we introduce Global-MPPI, a unified trajectory optimization framework that integrates global exploration and local refinement. At the global level, we leverage kernel sum-of-squares optimization to identify globally promising regions of the solution space. To enable reliable performance for the non-smooth landscapes inherent to contact-rich manipulation, we introduce a graduated non-convexity strategy based on log-sum-exp smoothing, which transitions the optimization landscape from a smoothed surrogate to the original non-smooth objective. Finally, we employ the model-predictive path integral method to locally refine the solution. We evaluate Global-MPPI on high-dimensional, long-horizon contact-rich tasks, including the PushT task and dexterous in-hand manipulation. Experimental results demonstrate that our approach robustly uncovers high-quality solutions, achieving faster convergence and lower final costs compared to existing baseline methods.

Zhongqi Wei, Frederike Dümbgen

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Inria, Département d’informatique de l’ENS, CNRS, PSL Research University, Paris, France

Trajectory OptimizationLong-Horizon Robotic ManipulationContact-Rich Robotic Manipulation
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Apr 26, 2026

Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation

We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coarse relocation (move) and contact-critical interaction (operate). Unlike monolithic policies that conflate these heterogeneous regimes, our architecture employs a dual-expert policy routed by a learnable phase selector, introducing a structural inductive bias that isolates phase-specific dynamics. Phase labels are automatically generated via an MLLM-based pipeline conditioned on lightweight contextual cues such as end-effector velocity and subtask decomposition to ensure alignment with human motor patterns. Evaluated on the RoboTwin2 benchmark, our method achieves an average success rate of 68.9%68.9\%, outperforming the monolithic π0π_0 baseline by 24%24\%. It matches or exceeds models trained on 10×10\times more data and reaches peak performance in 40%40\% fewer training steps, demonstrating that architectural disentanglement of move and operate phases is a highly effective and efficient strategy for mastering high-precision manipulation. The code is available at: https://github.com/healenrens/Move-then-Operate

Haoming Xu, Lei Lei, Jie Gu +3

Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China. · Rightly Robotics, Hangzhou, China. · Shanghai Innovation Institute, Shanghai, China. +2

Robot Skill LearningContact-Rich Robotic ManipulationMixture of ExpertsRobotic ManipulationVision-Language-Action Models
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Apr 26, 2026

Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation

Contact-rich manipulation is central to many everyday human activities, requiring continuous adaptation to contact uncertainty and external disturbances through multi-modal perception, particularly vision and tactile feedback. While imitation learning has shown strong potential for learning complex manipulation behaviors, most existing approaches rely on action chunking, which fundamentally limits their ability to react to unforeseen observations during execution. This limitation becomes especially critical in contact-rich scenarios, where physical uncertainty and high-frequency tactile feedback demand rapid, reactive control. To address this challenge, we propose Tube Diffusion Policy (TDP), a novel reactive visual-tactile policy learning framework that bridges diffusion-based imitation learning with tube-based feedback control. By leveraging the expressive power of generative models, TDP learns an observation-conditioned feedback flow around nominal action chunks, forming an action tube that enables fast and adaptive reactions during execution. We evaluate TDP on the widely used Push-T benchmark and three additional challenging visual-tactile dexterous manipulation tasks. Across all benchmarks, TDP consistently outperforms state-of-the-art imitation learning baselines. Two real-world experiments further validate its robust reactivity under contact uncertainty and external disturbances. Moreover, the step-wise correction mechanism enabled by action tube significantly reduces the required denoising steps, making TDP well suited for real-time, high-frequency feedback control in contact-rich manipulation.

Teng Xue, Alberto Rigo, Bingjian Huang +4

Meta Reality Labs Research · Idiap Research Institute · École Polytechnique Fédérale de Lausanne (EPFL) +2

Robot Policy LearningContact-Rich Robotic ManipulationRobust ControlDiffusion PolicyImitation Learning
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Apr 22, 2026

Visual-Tactile Peg-in-Hole Assembly Learning from Peg-out-of-Hole Disassembly

Peg-in-hole (PiH) assembly is a fundamental yet challenging robotic manipulation task. While reinforcement learning (RL) has shown promise in tackling such tasks, it requires extensive exploration. In this paper, we propose a novel visual-tactile skill learning framework for the PiH task that leverages its inverse task, i.e., peg-out-of-hole (PooH) disassembly, to facilitate PiH learning. Compared to PiH, PooH is inherently easier as it only needs to overcome existing friction without precise alignment, making data collection more efficient. To this end, we formulate both PooH and PiH as Partially Observable Markov Decision Processes (POMDPs) in a unified environment with shared visual-tactile observation space. A visual-tactile PooH policy is first trained; its trajectories, containing kinematic, visual and tactile information, are temporally reversed and action-randomized to provide expert data for PiH. In the policy learning, visual sensing facilitates the peg-hole approach, while tactile measurements compensate for peg-hole misalignment. Experiments across diverse peg-hole geometries show that the visual-tactile policy attains 6.4% lower contact forces than its single-modality counterparts, and that our framework achieves average success rates of 87.5% on seen objects and 77.1% on unseen objects, outperforming direct RL methods that train PiH policies from scratch by 18.1% in success rate. Demos, code, and datasets are available at https://sites.google.com/view/pooh2pih.

Yongqiang Zhao, Xuyang Zhang, Zhuo Chen +3

Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom · Department of Informatics, King’s College London, Strand, London, WC2R 2LS, United Kingdom

RL for RoboticsContact-Rich Robotic ManipulationRobotic ManipulationRobot Imitation LearningRobotic Assembly
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Apr 21, 2026

Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation tasks. Commonly, these methods learn neural control policies that predict end-effector pose changes directly from observed state information. For tasks like inserting delicate connectors which induce force constraints, pose-based policies have limited explicit control over force and rely on carefully tuned low-level controllers to avoid executing damaging actions. In this work, we present hybrid position-force control policies that learn to dynamically select when to use force or position control in each control dimension. To improve learning efficiency of these policies, we introduce Mode-Aware Training for Contact Handling (MATCH) which adjusts policy action probabilities to explicitly mirror the mode selection behavior in hybrid control. We validate MATCH's learned policy effectiveness using fragile peg-in-hole tasks under extreme localization uncertainty. We find MATCH substantially outperforms pose-control policies -- solving these tasks with up to 10% higher success rates and 5x fewer peg breaks than pose-only policies under common types of state estimation error. MATCH also demonstrates data efficiency equal to pose-control policies, despite learning in a larger and more complex action space. In over 1600 sim-to-real experiments, we find MATCH succeeds twice as often as pose policies in high noise settings (33% vs.~68%) and applies ~30% less force on average compared to variable impedance policies on a Franka FR3 in laboratory conditions.

Hunter L. Brown, Geoffrey Hollinger, Stefan Lee

Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR

Robotic ControlContact-Rich Robotic ManipulationRobotic RLRobotic InsertionForce-Controlled Robotic ManipulationRobust RL
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Apr 20, 2026

DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation

What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult, and the complexity is amplified in dual-arm settings. Such tasks are highly relevant to service robotics in domains such as hotels and hospitality, where robots must transport and reposition diverse objects with precision. We present DART, a novel dual-arm framework that integrates nonlinear Model Predictive Control (MPC) with an optimization-based impedance controller to achieve accurate object motion relative to a dynamically controlled tray. The framework systematically evaluates three complementary strategies for modeling tray-object dynamics as the state transition function within our MPC formulation: (i) a physics-based analytical model, (ii) an online regression based identification model that adapts in real-time, and (iii) a reinforcement learning-based dynamics model that generalizes across object properties. Our pipeline is validated in simulation with objects of varying mass, geometry, and friction coefficients. Extensive evaluations highlight the trade-offs among the three modeling strategies in terms of settling time, steady-state error, control effort, and generalization across objects. To the best of our knowledge, DART constitutes the first framework for non-prehensile dual-arm manipulation of objects on a tray. Project Link: https://dart-icra.github.io/dart/

Autrio Das, Shreya Bollimuntha, Madala Venkata Renu Jeevesh +5

Non-Prehensile Robotic ManipulationBimanual Robotic ManipulationNon-Prehensile ManipulationContact-Rich Robotic ManipulationNonlinear MPCModel Predictive Control+2
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Apr 19, 2026

Novel Algorithms for Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Construction

Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. Developing methods that make use of first/second order information about rigid-body dynamics in the presence of contact holds great promise in terms of increasing the solution speed and computational efficiency. The main bottleneck in this research direction is the difficulty in obtaining gradients and Hessians that are actually useful for numerical optimization, due to pathologies in all three steps of a common simulation pipeline: i) collision detection, ii) contact dynamics, iii) time integration. This abstract proposes a method that aims to address the collision detection part of the puzzle, via a novel pipeline designed from scratch with smooth (i.e. twice) differentiability and massive vectorizability on GPUs as the main priorities. This is in contrast to standard collision detection routines that are instead optimized for runtime on CPUs and minimal memory footprint, but do employ logic and control flow that hinder differentiability and vectorization. The proposed pipeline consists of the following contributions: i) highly expressive and compute efficient SDF representations, ii) differentiable broad-phase and narrow-phase routines that use these representations to generate vertex-SDF and edge-SDF contacts, iii) a differentiable routine for convex decomposition based contact blending.

Onur Beker, Andreas René Geist, Anselm Paulus +1

University of Tübingen

Signed Distance FunctionsContact-Rich Robotic ManipulationDifferentiable ProgrammingDifferentiable Physics
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Apr 19, 2026

FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes

Simulation frameworks such as Isaac Sim have enabled scalable robot learning for locomotion and rigid-body manipulation; however, contact-rich simulation remains a major bottleneck for deformable object manipulation. The continuously changing geometry of soft materials, together with large numbers of vertices and contact constraints, makes it difficult to achieve high accuracy, speed, and stability required for large-scale interactive learning. We present FLASH, a GPU-native simulation framework for contact-rich deformable manipulation, built on an accurate NCP-based solver that enforces strict contact and deformation constraints while being explicitly designed for fine-grained GPU parallelism. Rather than porting conventional single-instruction-multiple-data (SIMD) solvers to GPUs, FLASH redesigns the physics engine from the ground up to leverage modern GPU architectures, including optimized collision handling and memory layouts. As a result, FLASH scales to over 3 million degrees of freedom at 30 FPS on a single RTX 5090, while accurately simulating physical interactions. Policies trained solely on FLASH-generated synthetic data in minutes achieve robust zero-shot sim-to-real transfer, which we validate on physical robots performing challenging deformable manipulation tasks such as towel folding and garment folding, without any real-world demonstration, providing a practical alternative to labor-intensive real-world data collection.

Siyuan Luo, Bingyang Zhou, Chong Zhang +9

NUS Human-Centered Robotic Lab · ETH · ShanghaiTech University +2

Contact-Rich Robotic ManipulationRobotics SimulationDeformable Object ManipulationSynthetic Data Generation for RoboticsPhysics-Based SimulationDeformable Object Simulation
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Apr 18, 2026

Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.

Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2

OMRON SINIC X Corporation, Bunkyo-ku, Tokyo 113-0033, Japan · Waseda University, Shinjuku-ku, Tokyo, 169-8050, Japan · University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan

Robot Trajectory TrackingContact-Rich Robotic ManipulationLearning from DemonstrationRobot Imitation LearningIterative Learning ControlAdaptive Control
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Apr 14, 2026

Learning Versatile Humanoid Manipulation with Touch Dreaming

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity, and contact-aware interaction under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based lower-body controller that serves as the stability backbone for whole-body execution during complex manipulation. Building on this controller, we develop a VR-based whole-body humanoid data collection system that integrates dexterous hands and tactile sensing for contact-rich manipulation. We then propose Humanoid Transformer with Touch Dreaming (HTD), a multimodal encoder-decoder Transformer that models touch as a core modality alongside multi-view vision and proprioception. HTD is trained in a single stage with behavioral cloning augmented by touch dreaming: in addition to predicting action chunks, the policy predicts future hand-joint forces and future tactile latents, with tactile-latent targets provided by an exponential moving average target encoder without requiring a separate tactile pretraining stage. This encourages the policy to learn contact-aware representations for dexterous manipulation. Across five real-world contact-rich tasks, HTD achieves a 90.9% relative improvement in average success rate over the stronger baseline for each task. Ablation results further show that latent-space tactile prediction is more effective than raw tactile prediction, yielding a 30% relative gain in success rate. These results demonstrate that our touch-dreaming-enhanced learning system enables versatile, high-dexterity humanoid manipulation in the real world. More information and open-source materials are available at humanoid-touch-dream.github.io.

Yaru Niu, Zhenlong Fang, Binghong Chen +8

Carnegie Mellon University · Bosch Center for AI · UT Arlington

Humanoid Loco-ManipulationContact-Rich Robotic ManipulationWhole-Body Robotic ManipulationTactile Sensing for Robotic ManipulationRobot Imitation Learning
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Apr 14, 2026

Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact

Force and torque (F/T) sensors enable contact-aware control by providing reactive feedback, but they are often fragile and expensive. To overcome these limitations, sensorless methods estimate F/T or wrench solely from robot proprioception, and have shown success in slow interaction tasks such as grasping. However, their low-pass characteristics limit the estimation of high-frequency signals, which are critical in rapid-contact tasks such as grinding. Communication delays can also make their estimates outdated during deployment, but few methods address this directly. To bridge these gaps, we propose a Frequency-aware Decomposition Network (FDN) to estimate vibration-rich wrench in a sensorless, multi-step-ahead manner. Considering higher-frequency stochasticity, FDN spectrally decomposes the wrench horizon into a low-frequency trend and a high-frequency residual, and estimates each by pointwise regression and a learned conditional distribution, respectively. The frequency-aware layers impose band decomposition priors on the outputs and adaptively enhance frequency amplitudes of the inputs. FDN requires neither an identified robot model nor an F/T sensor during estimation. On real-world grinding data from our 6-DoF hydraulic manipulator, FDN reduces high-frequency amplitude error by up to 47% over the baselines under assumed time delays and maintains competitive low-frequency pointwise accuracy, while the baselines fail to balance these two. We also find multi-step-ahead estimation feasible, with FDN estimating a 1,000 ms horizon within 11 ms on a single CPU thread. Ablation studies further support our design choices. In an exploratory study, transferring wrench dynamics learned from an open-source everyday manipulation dataset reduces low-frequency error by 8%, while high-frequency dynamics appear domain-specific.

Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu +4

Department of Mechanical Engineering, Kyung Hee University, South Korea

Contact-Rich Robotic ManipulationFrequency-Domain Feature LearningRobotic Manipulation
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Apr 12, 2026

OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction

UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physical interaction signals. This becomes a fundamental limitation in contact-rich manipulation, where success depends on contact dynamics such as tactile interaction, internal grasping force, and external interaction wrench that are difficult to infer from vision alone. We present OmniUMI, a unified framework for physically grounded robot learning via human-aligned multimodal interaction. OmniUMI synchronously captures RGB, depth, trajectory, tactile sensing, internal grasping force, and external interaction wrench within a compact handheld system, while maintaining collection--deployment consistency through a shared embodiment design. To support human-aligned demonstration, OmniUMI enables natural perception and modulation of internal grasping force, external interaction wrench, and tactile interaction through bilateral gripper feedback and the handheld embodiment. Built on this interface, we extend diffusion policy with visual, tactile, and force-related observations, and deploy the learned policy through impedance-based execution for unified regulation of motion and contact behavior. Experiments demonstrate reliable sensing and strong downstream performance on force-sensitive pick-and-place, interactive surface erasing, and tactile-informed selective release. Overall, OmniUMI combines physically grounded multimodal data acquisition with human-aligned interaction, providing a scalable foundation for learning contact-rich manipulation.

Shaqi Luo, Yuanyuan Li, Youhao Hu +7

Beijing Academy of Artificial Intelligence · MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +3

Robot LearningContact-Rich Robotic ManipulationRobotic Data CollectionForce-Controlled Robotic ManipulationTactile Sensing for Robotic ManipulationHuman-Robot Interaction
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Apr 8, 2026

TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks

Handheld paradigms offer an efficient and intuitive way for collecting large-scale demonstration of robot manipulation. However, achieving contact-rich bimanual manipulation through these methods remains a pivotal challenge, which is substantially hindered by hardware adaptability and data efficacy. Prior hardware designs remain gripper-specific and often face a trade-off between tracking precision and portability. Furthermore, the lack of online feasibility checking during demonstration leads to poor replayability. More importantly, existing handheld setups struggle to collect interactive recovery data during robot execution, lacking the authentic tactile information necessary for robust policy refinement. To bridge these gaps, we present TAMEn, a tactile-aware manipulation engine for closed-loop data collection in contact-rich tasks. Our system features a cross-morphology wearable interface that enables rapid adaptation across heterogeneous grippers. To balance data quality and environmental diversity, we implement a dual-modal acquisition pipeline: a precision mode leveraging motion capture for high-fidelity demonstrations, and a portable mode utilizing VR-based tracking for in-the-wild acquisition and tactile-visualized recovery teleoperation. Building on this hardware, we unify large-scale tactile pretraining, task-specific bimanual demonstrations, and human-in-the-loop recovery data into a pyramid-structured data regime, enabling closed-loop policy refinement. Experiments show that our feasibility-aware pipeline significantly improves demonstration replayability, and that the proposed visuo-tactile learning framework increases task success rates from 34% to 75% across diverse bimanual manipulation tasks. We further open-source the hardware and dataset to facilitate reproducibility and support research in visuo-tactile manipulation.

Longyan Wu, Jieji Ren, Chenghang Jiang +6

Fudan University · Shanghai Innovation Institute · OpenDriveLab at The University of Hong Kong +2

Robotic Data CollectionContact-Rich Robotic ManipulationBimanual Robotic ManipulationVisuo-Tactile Representation LearningCross-Embodiment Robot Learning
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Apr 8, 2026

Learning-Based Strategy for Composite Robot Assembly Skill Adaptation

Contact-rich robotic skills remain challenging for industrial robots due to tight geometric tolerances, frictional variability, and uncertain contact dynamics, particularly when using position-controlled manipulators. This paper presents a reusable and encapsulated skill-based strategy for peg-in-hole assembly, in which adaptation is achieved through Residual Reinforcement Learning (RRL). The assembly process is represented using composite skills with explicit pre-, post-, and invariant conditions, enabling modularity, reusability, and well-defined execution semantics across task variations. Safety and sample efficiency are promoted through RRL by restricting adaptation to residual refinements within each skill during contact-rich interactions, while the overall skill structure and execution flow remain invariant. The proposed approach is evaluated in MuJoCo simulation on a UR5e robot equipped with a Robotiq gripper and trained using SAC and JAX. Results demonstrate that the proposed formulation enables robust execution of assembly skills, highlighting its suitability for industrial automation.

Khalil Abuibaid, Aleksandr Sidorenko, Achim Wagner +1

Chair of Machine Tools and Control System, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany · Innovative Factory Systems, German Institute of Artificial Intelligence, 67663 Kaiserslautern, Germany · Chair of Machine Tools and Control Systems, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany

Robot Skill LearningRobotic AssemblyContact-Rich Robotic ManipulationIndustrial Robotics
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Apr 7, 2026

Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring

Model Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation, and collision-free circular motions in challenging configurations, which exceeds the capability of standard MPC pipelines. We propose a framework that integrates force-feedback MPC with diffusion-based motion priors to address these challenges. The diffusion model serves as a memory of motion strategies, providing robust initialization and adaptation across multiple task instances, while MPC ensures safe execution with explicit force tracking, torque feasibility, and collision avoidance. We validate our approach on a torque-controlled manipulator performing industrial deburring tasks. Experiments demonstrate reliable tool insertion, accurate normal force tracking, and circular deburring motions even in hard-to-reach configurations and under obstacle constraints. To our knowledge, this is the first integration of diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial tasks.

Krzysztof Wojciechowski, Ege Gursoy, Arthur Haffemayer +4

LAAS-CNRS, Universite de Toulouse, CNRS, Toulouse, France · Inria, AUCTUS team, Talence, France · Image and Pervasive Access Laboratory (IPAL), CNRS-UMI, 2955, Singapore +1

Force-Controlled Robotic ManipulationContact-Rich Robotic ManipulationModel Predictive ControlRobotic ManipulationIndustrial Robotics
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Apr 7, 2026

You're Pushing My Buttons: Instrumented Learning of Gentle Button Presses

Learning contact-rich manipulation is difficult from cameras and proprioception alone because contact events are only partially observed. We test whether training-time instrumentation, i.e., object sensorisation, can improve policy performance without creating deployment-time dependencies. Specifically, we study button pressing as a testbed and use a microphone fingertip to capture contact-relevant audio. We use an instrumented button-state signal as privileged supervision to fine-tune an audio encoder into a contact event detector. We combine the resulting representation with imitation learning using three strategies, such that the policy only uses vision and audio during inference. Button press success rates are similar across methods, but instrumentation-guided audio representations consistently reduce contact force. These results support instrumentation as a practical training-time auxiliary objective for learning contact-rich manipulation policies.

Raman Talwar, Remko Proesmans, Thomas Lips +2

AI and Robotics Lab (IDLab-AIRO), Ghent University—imec, Ghent, Belgium

Contact-Rich Robotic ManipulationImitation Learning
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Apr 7, 2026

Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand

Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact with container surfaces during scooping, improving efficiency compared to rigid tools. These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control. However, the inherent compliance and complex deformation behavior of soft robotics introduce significant control complexity that limits practical applications. To address this challenge, this paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand that captures its passive reconfiguration dynamics and enables systematic trajectory optimization for scooping tasks. We propose a novel physics-based simulation approach that accurately models the soft tool's morphing behavior from flat sheets to adaptive conical structures, combined with an evolutionary strategy framework that automatically optimizes scooping trajectories without manual parameter tuning. We validate the optimized trajectories through both simulation and real-robot experiments. The results demonstrate strong generalization and successfully address a range of challenging tasks previously beyond the reach of existing approaches. Videos of our experiments are available online: https://sites.google.com/view/scoopsh

Yongliang Wang, Cristian C. Beltran-Hernandez, Tomoya Takahashi +1

OMRON SINIC X Corporation, Tokyo, Japan. · Department of Artificial Intelligence, Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, The Netherlands

Robotic ManipulationContact-Rich Robotic ManipulationSoft RoboticsRobotics SimulationTrajectory Optimization
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Apr 2, 2026

MorphoGuard: A Morphology-Based Whole-Body Interactive Motion Controller

Whole-body control (WBC) has demonstrated significant advantages in complex interactive movements of high-dimensional robotic systems. However, when a robot is required to handle dynamic multi-contact combinations along a single kinematic chain-such as pushing open a door with its elbow while grasping an object-it faces major obstacles in terms of complex contact representation and joint configuration coupling. To address this, we propose a new control approach that explicitly manages arbitrary contact combinations, aiming to endow robots with whole-body interactive capabilities. We develop a morphology-constrained WBC network (MorphoGuard)-which is trained on a self-constructed dual-arm physical and simulation platform. A series of model recommendation experiments are designed to systematically investigate the impact of backbone architecture, fusion strategy, and model scale on network performance. To evaluate the control performance, we adopt a multi-object interaction task as the benchmark, requiring the model to simultaneously manipulate multiple target objects to specified positions. Experimental results show that the proposed method achieves a contact point management error of approximately 1 cm, demonstrating its effectiveness in whole-body interactive control.

Chenjin Wang, Zheng Yan, Yanmin Zhou +2

Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210, China · College of Electronics and Information Engineering (Tongji University) Shanghai 201210, China

Robotic ControlWhole-Body Robotic ManipulationWhole-Body ControlContact-Rich Robotic Manipulation
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Apr 1, 2026

Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation

Vision-based policies have achieved a good performance in robotic manipulation due to the accessibility and richness of visual observations. However, purely visual sensing becomes insufficient in contact-rich and force-sensitive tasks where force/torque (F/T) signals provide critical information about contact dynamics, alignment, and interaction quality. Although various strategies have been proposed to integrate vision and F/T signals, including auxiliary prediction objectives, mixture-of-experts architectures, and contact-aware gating mechanisms, a comparison of these approaches remains lacking. In this work, we provide a controlled comparison of different F/T-vision integration strategies within diffusion-based manipulation policies. In addition, we propose an adaptive integration strategy that ignores F/T signals during non-contact phases while adaptively leveraging both vision and torque information during contact. Experimental results demonstrate that our method outperforms the strongest baseline by 14% in success rate, highlighting the importance of contact-aware multimodal fusion for robotic manipulation.

Jiuzhou Lei, Chang Liu, Yu She +2

Jiuzhou Lei, Chang Liu, and Minghui Zheng are with the J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA. · Yu She is with the Edwardson School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA. · Xiao Liang is with the Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA.

Robotic ManipulationContact-Rich Robotic ManipulationMultimodal Sensor FusionDiffusion Policy
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Apr 1, 2026

Functional Force-Aware Retargeting from Virtual Human Demos to Soft Robot Policies

We introduce SoftAct, a framework for teaching soft robot hands to perform human-like manipulation skills by explicitly reasoning about contact forces. Leveraging immersive virtual reality, our system captures rich human demonstrations, including hand kinematics, object motion, dense contact patches, and detailed contact force information. Unlike conventional approaches that retarget human joint trajectories, SoftAct employs a two-stage, force-aware retargeting algorithm. The first stage attributes demonstrated contact forces to individual human fingers and allocates robot fingers proportionally, establishing a force-balanced mapping between human and robot hands. The second stage performs online retargeting by combining baseline end-effector pose tracking with geodesic-weighted contact refinements, using contact geometry and force magnitude to adjust robot fingertip targets in real time. This formulation enables soft robotic hands to reproduce the functional intent of human demonstrations while naturally accommodating extreme embodiment mismatch and nonlinear compliance. We evaluate SoftAct on a suite of contact-rich manipulation tasks using a custom non-anthropomorphic pneumatic soft robot hand. SoftAct's controller reduces fingertip trajectory tracking RMSE by up to 55 percent and reduces tracking variance by up to 69 percent compared to kinematic and learning-based baselines. At the policy level, SoftAct achieves consistently higher success in zero-shot real-world deployment and in simulation. These results demonstrate that explicitly modeling contact geometry and force distribution is essential for effective skill transfer to soft robotic hands, and cannot be recovered through kinematic imitation alone. Project videos and additional details are available at https://soft-act.github.io/.

Uksang Yoo, Mengjia Zhu, Evan Pezent +8

The Robotics Institute, Carnegie Mellon University · Meta Reality Labs

Motion RetargetingRobot Imitation LearningCross-Embodiment Robot LearningSoft RoboticsContact-Rich Robotic ManipulationZero-Shot Robotic Manipulation
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Apr 1, 2026

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distribution. This limitation is especially important for reliable robot planning and control in contact-rich tasks such as pushing and pick-and-place, where changes in goals, contact conditions, or robot dynamics can drive the system out-of-distribution at inference time. In this paper, we investigate a hybrid controller that combines reinforcement learning with bounded extremum seeking (ES) to improve robustness under such conditions. In the proposed approach, deep deterministic policy gradient (DDPG) policies are trained under standard conditions on the robotic pushing and pick-and-place tasks, and are then combined with bounded ES during deployment. The RL policy provides fast manipulation behavior, while bounded ES ensures robustness of the overall controller to time variations when operating conditions depart from those seen during training. The resulting controller is evaluated under several out-of-distribution settings, including time-varying goals and spatially varying friction patches. Crucially, under reasonable assumptions, we prove finite time RL-based convergence of a robotic arm to an object after which bounded ES achieves finite-time convergence to a bounded velocity moving target. We provide analytic formulas for both convergence times, and an ability to tune the convergence times by our choice of gains.

Shaifalee Saxena, Rafael Fierro, Alexander Scheinker

Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA · Los Alamos National Lab, Los Alamos, NM 87547, USA

Robotic ControlReinforcement LearningContact-Rich Robotic ManipulationGeneralization in Robotic ManipulationFeedback ControlRobust RL
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Mar 31, 2026

HapCompass: A Rotational Haptic Device for Contact-Rich Robotic Teleoperation

The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive directional cues within wearable teleoperation interfaces remains a bottleneck. Existing solutions, such as non-directional vibrations from handheld controllers, provide limited information, while vibrotactile arrays are prone to perceptual interference. To address these limitations, we propose HapCompass, a novel, low-cost wearable haptic device that renders 2D directional cues by mechanically rotating a single linear resonant actuator (LRA). We evaluated HapCompass's ability to convey directional cues to human operators and showed that it increased the success rate, decreased the completion time and the maximum contact force for teleoperated manipulation tasks when compared to vision-only and non-directional feedback baselines. Furthermore, we conducted a preliminary imitation-learning evaluation, suggesting that the directional feedback provided by HapCompass enhances the quality of demonstration data and, in turn, the trained policy. We release the design of the HapCompass device along with the code that implements our teleoperation interface: https://ripl.github.io/HapCompass/.

Xiangshan Tan, Jingtian Ji, Tianchong Jiang +2

Toyota Technological Institute at Chicago (TTIC) · University of Chicago

Haptic Feedback for Robot TeleoperationHaptic FeedbackRobot TeleoperationContact-Rich Robotic Manipulation
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Mar 29, 2026

Robotic Dexterous Manipulation via Anisotropic Friction Modulation using Passive Rollers

Controlling friction at the fingertip is fundamental to dexterous manipulation, yet remains difficult to realize in robotic hands. We present the design and analysis of a robotic fingertip equipped with passive rollers that can be selectively braked or pivoted to modulate contact friction and constraint directions. When unbraked, the rollers permit unconstrained sliding of the contact point along the rolling direction; when braked, they resist motion like a conventional fingertip. The rollers are mounted on a pivoting mechanism, allowing reorientation of the constraint frame to accommodate different manipulation tasks. We develop a constraint-based model of the fingertip integrated into a parallel-jaw gripper and analyze its ability to support diverse manipulation strategies. Experiments show that the proposed design enables a wide range of dexterous actions that are conventionally challenging for robotic grippers, including sliding and pivoting within the grasp, robust adaptation to uncertain contacts, multi-object or multi-part manipulation, and interactions requiring asymmetric friction across fingers. These results demonstrate the versatility of passive roller fingertips as a low-complexity, mechanically efficient approach to friction modulation, advancing the development of more adaptable and robust robotic manipulation.

Ethan Fisk, Taeyoon Lee, Shenli Yuan

Department of Mechanical Engineering, Northeastern University, Boston, MA 02115, USA · RAI Institute, Cambridge, MA 02142, USA

Robotic ManipulationDexterous ManipulationRobotic Hand DesignContact-Rich Robotic ManipulationIn-Hand Manipulation
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Mar 26, 2026

Visualizing Impedance Control in Augmented Reality for Teleoperation: Design and User Evaluation

Teleoperation for contact-rich manipulation remains challenging, especially when using low-cost, motion-only interfaces that provide no haptic feedback. Virtual reality controllers enable intuitive motion control but do not allow operators to directly perceive or regulate contact forces, limiting task performance. To address this, we propose an augmented reality (AR) visualization of the impedance controller's target pose and its displacement from each robot end effector. This visualization conveys the forces generated by the controller, providing operators with intuitive, real-time feedback without expensive haptic hardware. We evaluate the design in a dual-arm manipulation study with 17 participants who repeatedly reposition a box with and without the AR visualization. Results show that AR visualization reduces completion time by 24% for force-critical lifting tasks, with no significant effect on sliding tasks where precise force control is less critical. These findings indicate that making the impedance target visible through AR is a viable approach to improve human-robot interaction for contact-rich teleoperation.

Gijs van den Brandt, Femke van Beek, Elena Torta

Department of Mechanical Engineering, Eindhoven University of Technology, The Netherlands

Bimanual Robotic ManipulationContact-Rich Robotic ManipulationRobot TeleoperationImpedance ControlHuman-Robot Interaction
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Mar 26, 2026

A Minimum-Energy Control Approach for Redundant Mobile Manipulators in Physical Human-Robot Interaction Applications

Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which could not be performed using fixed-base manipulators. Within this context, developing suitable control methodologies is essential since mobile manipulators introduce additional degrees of freedom, making the design of control approaches more challenging and more prone to performance optimization. This paper proposes a control approach for a mobile manipulator, composed of a mobile base equipped with a robotic arm mounted on the top, with the objective of minimizing the overall kinetic energy stored in the whole-body mobile manipulator in physical human-robot interaction applications. The approach is experimentally tested with reference to a peg-in-hole task, and the results demonstrate that the proposed approach reduces the overall kinetic energy stored in the whole-body robotic system and improves the system performance compared with the benchmark method.

Davide Tebaldi, Niccolò Paradisi, Fabio Pini +1

The authors are with the “Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, via Pietro Vivarelli 10, 41125 Modena, Italy · The author is with the Department of Engineering for Innovation Medicine, University of Verona

Robotic ControlContact-Rich Robotic ManipulationHuman-Robot Interaction
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Mar 24, 2026

VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs

Video-Action Models (VAMs) have emerged as a promising framework for embodied intelligence, learning implicit world dynamics from raw video streams to produce temporally consistent action predictions. Although such models demonstrate strong performance on long-horizon tasks through visual reasoning, they remain limited in contact-rich scenarios where critical interaction states are only partially observable from vision alone. In particular, fine-grained force modulation and contact transitions are not reliably encoded in visual tokens, leading to unstable or imprecise behaviors. To bridge this gap, we introduce the Video-Tactile Action Model (VTAM), a multimodal world modeling framework that incorporates tactile perception as a complementary grounding signal. VTAM augments a pretrained video transformer with tactile streams via a lightweight modality transfer finetuning, enabling efficient cross-modal representation learning without tactile-language paired data or independent tactile pretraining. To stabilize multimodal fusion, we introduce a tactile regularization loss that enforces balanced cross-modal attention, preventing visual latent dominance in the action model. VTAM demonstrates superior performance in contact-rich manipulation, maintaining a robust success rate of 90 percent on average. In challenging scenarios such as potato chip pick-and-place requiring high-fidelity force awareness, VTAM outperforms the pi 0.5 baseline by 80 percent. Our findings demonstrate that integrating tactile feedback is essential for correcting visual estimation errors in world action models, providing a scalable approach to physically grounded embodied foundation models.

Haoran Yuan, Weigang Yi, Zhenyu Zhang +9

University of Illinois Urbana-Champaign · Stanford University · Shanghai Jiao Tong University

Tactile World ModelsContact-Rich Robotic ManipulationVisuo-Tactile Representation LearningTactile Sensing for Robotic Manipulation
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