Mobile Manipulation

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Overview

Mobile manipulation coordinates navigation, perception, and physical interaction so robots can reach objects, choose workable base positions, and perform tasks while moving between locations. Approaches range from geometric mapping, reachability analysis, motion planning, and whole-body model-predictive control to imitation learning, reinforcement learning, and vision-language-action policies trained on robot or human demonstrations and simulation data. Work increasingly combines these methods with hierarchical skills, spatial memory, active perception, and recovery for long-horizon tasks in homes and other changing environments. A central challenge is transferring success from individual skills and simulation to reliable real-world behavior under shifts in viewpoint, base pose, clutter, contact, and disturbances; aggregate benchmark scores can obscure these weaknesses.

Latest papers

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93 papers18 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Apr 28, 2026

ANCHOR: A Physically Grounded Closed-Loop Framework for Robust Home-Service Mobile Manipulation

Recent advances in open-vocabulary mobile manipulation have brought robots into real domestic environments. In such settings, reliable long-horizon execution under open-set object references and frequent disturbances becomes essential. However, many failures persist. These are not caused by semantic misunderstanding but by inconsistencies between symbolic plans and the evolving physical world, manifested as three recurring limitations: (i) existing systems often rely on pre-scanned semantic maps that become inconsistent after scene changes and disturbances; (ii) they select navigation endpoints without considering downstream manipulation feasibility, causing the "arrived but inoperable" problem; and (iii) they handle anomalies through undifferentiated global replanning, which often fails to contain local errors. To address this execution inconsistency, we present ANCHOR, a physically grounded closed-loop framework that aligns symbolic reasoning with verifiable physical state during execution. ANCHOR integrates three mechanisms: (i) physically anchored task planning, which binds symbolic predicates to observable geometric anchors and re-validates them after each action; (ii) operability-aware base alignment, which ensures that navigation endpoints satisfy kinematic reachability and local collision feasibility; and (iii) minimum-responsible-layer hierarchical recovery, which localizes failures across perception, base-arm coordination, and execution layers to prevent cascading retries. Across 60 real-robot trials in previously unseen environments, ANCHOR improves task success from 53.3% to 71.7% and achieves a 71.4% recovery rate under perturbations, demonstrating that explicit physical grounding and structured failure containment are critical for robust mobile manipulation. Our project page is available at https://anchor9178.github.io/ANCHOR/ .

Jinhao Jiang, Shengyu Fang, Sibo Zuo +2

Beijing Institute of Technology

Long-Horizon Robotic ManipulationMobile ManipulationRobot Failure RecoveryRobot Task Planning
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Apr 21, 2026

GenerativeMPC: VLM-RAG-guided Whole-Body MPC with Virtual Impedance for Bimanual Mobile Manipulation

Bimanual mobile manipulation requires a seamless integration between high-level semantic reasoning and safe, compliant physical interaction - a challenge that end-to-end models approach opaquely and classical controllers lack the context to address. This paper presents GenerativeMPC, a hierarchical cyber-physical framework that explicitly bridges semantic scene understanding with physical control parameters for bimanual mobile manipulators. The system utilizes a Vision-Language Model with Retrieval-Augmented Generation (VLM-RAG) to translate visual and linguistic context into grounded control constraints, specifically outputting dynamic velocity limits and safety margins for a Whole-Body Model Predictive Controller (MPC). Simultaneously, the VLM-RAG module modulates virtual stiffness and damping gains for a unified impedance-admittance controller, enabling context-aware compliance during human-robot interaction. Our framework leverages an experience-driven vector database to ensure consistent parameter grounding without retraining. Experimental results in MuJoCo, IsaacSim, and on a physical bimanual platform confirm a 60% speed reduction near humans and safe, socially-aware navigation and manipulation through semantic-to-physical parameter grounding. This work advances the field of human-centric cybernetics by grounding large-scale cognitive models into predictable, high-frequency physical control loops.

Marcelino Julio Fernando, Miguel Altamirano Cabrera, Jeffrin Sam +3

Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia

Mobile ManipulationBimanual Robotic ManipulationWhole-Body Robotic ManipulationImpedance ControlModel Predictive ControlHuman-Robot Interaction+1
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Apr 16, 2026

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage paradigm, where the robot first navigates to a docking point and then performs fixed-base manipulation using powerful visuomotor policies. However, real-world mobile manipulation often suffers from the view generalization problem due to shifts of docking points. To address this issue, we propose a novel low-cost demonstration generation framework named DockAnywhere, which improves viewpoint generalization under docking variability by lifting a single demonstration to diverse feasible docking configurations. Specifically, DockAnywhere lifts a trajectory to any feasible docking points by decoupling docking-dependent base motions from contact-rich manipulation skills that remain invariant across viewpoints. Feasible docking proposals are sampled under feasibility constraints, and corresponding trajectories are generated via structure-preserving augmentation. Visual observations are synthesized in 3D space by representing the robot and objects as point clouds and applying point-level spatial editing to ensure the consistency of observation and action across viewpoints. Extensive experiments on ManiSkill and real-world platforms demonstrate that DockAnywhere substantially improves policy success rates and easily generalizes to novel viewpoints from unseen docking points during training, significantly enhancing the generalization capability of mobile manipulation policy in real-world deployment.

Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3

Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore

Mobile ManipulationGeneralization in Robotic ManipulationVisuomotor Policy LearningSynthetic Data Generation for RoboticsRobotic ManipulationRobot Policy Generalization
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Apr 15, 2026

ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation

Coordinating navigation and manipulation with robust performance is essential for embodied AI in complex indoor environments. However, as tasks extend over long horizons, existing methods often struggle due to catastrophic forgetting, spatial inconsistency, and rigid execution. To address these issues, we propose ESCAPE (Episodic Spatial Memory Coupled with an Adaptive Policy for Execution), operating through a tightly coupled perception-grounding-execution workflow. For robust perception, ESCAPE features a Spatio-Temporal Fusion Mapping module to autoregressively construct a depth-free, persistent 3D spatial memory, alongside a Memory-Driven Target Grounding module for precise interaction mask generation. To achieve flexible action, our Adaptive Execution Policy dynamically orchestrates proactive global navigation and reactive local manipulation to seize opportunistic targets. ESCAPE achieves state-of-the-art performance on the ALFRED benchmark, reaching 65.09% and 60.79% success rates in test seen and unseen environments with step-by-step instructions. By reducing redundant exploration, our ESCAPE attains substantial improvements in path-length-weighted metrics and maintains robust performance (61.24% / 56.04%) even without detailed guidance for long-horizon tasks.

Jingjing Qian, Zeyuan He, Chen Shi +2

The Chinese University of Hong Kong, Shenzhen · LiAuto Inc.

Robotic Spatial MemoryLong-Horizon Robotic ManipulationMobile ManipulationRoboticsRobot Policy Learning
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Apr 14, 2026

FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators

Fast dexterous grasping while a mobile base remains in motion requires coordinated whole-body control and rapid adaptation to physical contact. We propose FastGrasp, a two-stage learning framework that integrates grasp guidance, whole-body control, and tactile feedback. First, a pretrained conditional variational autoencoder generates diverse grasp candidates from object point clouds. Candidates are filtered by approach direction and supporting-surface constraints, then ranked using an envelopment-based criterion combining grasp width and depth coverage measures. Second, a reinforcement learning policy jointly controls the mobile base, arm, and dexterous hand, using the selected grasp as guidance and tactile observations for online grasp adjustment. The policy is trained with domain randomization and deployed with command filtering and tactile-triggered grasp tightening. Simulation experiments show higher grasp success rates than the evaluated baselines under full and partial point-cloud observations, while real-world experiments demonstrate sim-to-real transfer across diverse object geometries.

Heng Tao, Yiming Zhong, Zemin Yang +1

ShanghaiTech University

RL for RoboticsRobotic GraspingMobile ManipulationWhole-Body Robotic ManipulationWhole-Body Control
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Apr 14, 2026

Scalable Trajectory Generation for Whole-Body Mobile Manipulation

Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed-base manipulation. Yet existing acquisition methods, including teleoperation and planning, are either labor-intensive or computationally prohibitive at scale. The core bottleneck is the lack of a scalable pipeline for generating large-scale, physically valid, coordinated trajectory data across diverse embodiments and environments. Here we introduce AutoMoMa, a GPU-accelerated framework that unifies AKR modeling, which consolidates base, arm, and object kinematics into a single chain, with parallelized trajectory optimization. AutoMoMa achieves 5,000 episodes per GPU-hour (over 80×80\times faster than CPU-based baselines), producing a dataset of over 500k physically valid trajectories spanning 330 scenes, diverse articulated objects, and multiple robot embodiments. Prior datasets were forced to compromise on scale, diversity, or kinematic fidelity; AutoMoMa addresses all three simultaneously. Training downstream IL policies further reveals that even a single articulated-object task requires tens of thousands of demonstrations for SOTA methods to reach ≈80%\approx 80\% success, confirming that data scarcity -- not algorithmic limitations -- has been the binding constraint. AutoMoMa thus bridges high-performance planning and reliable IL-based control, providing the infrastructure previously missing for coordinated mobile manipulation research. By making large-scale, kinematically valid training data practical, AutoMoMa showcases generalizable whole-body robot policies capable of operating in the diverse, unstructured settings of the real world.

Yida Niu, Xinhai Chang, Xin Liu +2

Robot Motion GenerationMobile ManipulationWhole-Body Robotic ManipulationSynthetic Data Generation for RoboticsTrajectory Optimization
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Apr 14, 2026

Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers

Mobile Manipulation (MoMa) of articulated objects, such as opening doors, drawers, and cupboards, demands simultaneous, whole-body coordination between a robot's base and arms. Classical whole-body controllers (WBCs) can solve such problems via hierarchical optimization, but require extensive hand-tuned optimization and remain brittle. Learning-based methods, on the other hand, show strong generalization capabilities but typically rely on expensive whole-body teleoperation data or heavy reward engineering. We observe that even a sub-optimal WBC is a powerful structural prior: it can be used to collect data in a constrained, task-relevant region of the state-action space, and its behavior can still be improved upon using offline reinforcement learning. Building on this, we propose WHOLE-MoMa, a two-stage pipeline that first generates diverse demonstrations by randomizing a lightweight WBC, and then applies offline RL to identify and stitch together improved behaviors via a reward signal. To support the expressive action-chunked diffusion policies needed for complex coordination tasks, we extend offline implicit Q-learning with Q-chunking for chunk-level critic evaluation and advantage-weighted policy extraction. On three tasks of increasing difficulty using a TIAGo++ mobile manipulator in simulation, WHOLE-MoMa significantly outperforms WBC, behavior cloning, and several offline RL baselines. Policies transfer directly to the real robot without finetuning, achieving 80% success in bimanual drawer manipulation and 68% in simultaneous cupboard opening and object placement, all without any teleoperated or real-world training data.

Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki

PEARL Lab, Computer Science Department, Technische Universität Darmstadt, Germany · Hessian.AI, Darmstadt, Germany · Robotics Institute, Germany

Mobile ManipulationWhole-Body Robotic ManipulationOffline RLBimanual Robotic Manipulation
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Apr 9, 2026

EMMa: End-Effector Stability-Oriented Mobile Manipulation for Tracked Rescue Robots

The autonomous operation of tracked mobile manipulators in rescue missions requires not only ensuring the reachability and safety of robot motion but also maintaining stable end-effector manipulation under diverse task demands. However, existing studies have overlooked many end-effector motion properties at both the planning and control levels. This paper presents a motion generation framework for tracked mobile manipulators to achieve stable end-effector operation in complex rescue scenarios. The framework formulates a coordinated path optimization model that couples end-effector and mobile base states and designs compact cost/constraint representations to mitigate nonlinearities and reduce computational complexity. Furthermore, an isolated control scheme with feedforward compensation and feedback regulation is developed to enable coordinated path tracking for the robot. Extensive simulated and real-world experiments on rescue scenarios demonstrate that the proposed framework consistently outperforms SOTA methods across key metrics, including task success rate and end-effector motion stability, validating its effectiveness and robustness in complex mobile manipulation tasks.

Yifei Wang, Hao Zhang, Jidong Huang +2

Mobile ManipulationRobot Motion GenerationRobotic ManipulationSearch-and-Rescue RoboticsRobot Trajectory Tracking
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Apr 7, 2026

Synergizing Efficiency and Reliability for Continuous Mobile Manipulation

Humans seamlessly fuse anticipatory planning with immediate feedback to perform successive mobile manipulation tasks without stopping, achieving both high efficiency and reliability. Replicating this fluid and reliable behavior in robots remains fundamentally challenging, not only due to conflicts between long-horizon planning and real-time reactivity, but also because excessively pursuing efficiency undermines reliability in uncertain environments: it impairs stable perception and the potential for compensation, while also increasing the risk of unintended contact. In this work, we present a unified framework that synergizes efficiency and reliability for continuous mobile manipulation. It features a reliability-aware trajectory planner that embeds essential elements for reliable execution into spatiotemporal optimization, generating efficient and reliability-promising global trajectories. It is coupled with a phase-dependent switching controller that seamlessly transitions between global trajectory tracking for efficiency and task-error compensation for reliability. We also investigate a hierarchical initialization that facilitates online replanning despite the complexity of long-horizon planning problems. Real-world evaluations demonstrate that our approach enables efficient and reliable completion of successive tasks under uncertainty (e.g., dynamic disturbances, perception and control errors). Moreover, the framework generalizes to tasks with diverse end-effector constraints. Compared with state-of-the-art baselines, our method consistently achieves the highest efficiency while improving the task success rate by 26.67%--81.67%. Comprehensive ablation studies further validate the contribution of each component. The source code will be released.

Chengkai Wu, Ruilin Wang, Yixin Zeng +7

Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China · Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

Mobile ManipulationRobotic ControlMotion PlanningLong-Horizon Robotic ManipulationRobot Trajectory Tracking
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Apr 7, 2026

ExpressMM: Expressive Mobile Manipulation Behaviors in Human-Robot Interactions

Mobile manipulators are increasingly deployed in human-centered environments to perform tasks. While completing such tasks, they should also be able to communicate their intent to the people around them using expressive robot behaviors. Prior work on expressive robot behaviors has used preprogrammed or learning-from-demonstration-based expressive motions and large language model generated high-level interactions. The majority of these existing approaches have not considered human-robot interactions (HRI) where users may interrupt, modify, or redirect a robot's actions during task execution. In this paper, we develop the novel ExpressMM framework that integrates a high-level language-guided planner based on a vision-language model for perception and conversational reasoning with a low-level vision-language-action policy to generate expressive robot behaviors during collaborative HRI tasks. Furthermore, ExpressMM supports interruptible interactions to accommodate updated or redirecting instructions by users. We demonstrate ExpressMM on a mobile manipulator assisting a human in a collaborative assembly scenario and conduct audience-based evaluation of live HRI demonstrations. Questionnaire results show that the ExpressMM-enabled expressive behaviors helped observers clearly interpret the robot's actions and intentions while supporting socially appropriate and understandable interactions. Participants also reported that the robot was useful for collaborative tasks and behaved in a predictable and safe manner during the demonstrations, fostering positive perceptions of the robot's usefulness, safety, and predictability during the collaborative tasks.

Souren Pashangpour, Haitong Wang, Matthew Lisondra +1

Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada

Human-Robot InteractionHuman-Robot CollaborationMobile ManipulationVLMs for Robotics
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Apr 5, 2026

Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators

We present a learning-enhanced motion planner for differential drive mobile manipulators to improve efficiency, success rate, and optimality. For task representation encoder, we propose a keypoint sequence extraction module that maps boundary states to 3D space via differentiable forward kinematics. Point clouds and keypoints are encoded separately and fused with attention, enabling effective integration of environment and boundary states information. We also propose a primitive-based truncated diffusion model that samples from a biased distribution. Compared with vanilla diffusion model, this framework improves the efficiency and diversity of the solution. Denoised paths are refined by trajectory optimization to ensure dynamic feasibility and task-specific optimality. In cluttered 3D simulations, our method achieves higher success rate, improved trajectory diversity, and competitive runtime compared to vanilla diffusion and classical baselines. The source code is released at https://github.com/nmoma/nmoma .

Long Xu, Choilam Wong, Yuhang Zhong +3

Diffusion-Based PlanningDiffusion Models for Motion GenerationMobile ManipulationKinodynamic Motion Planning
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Apr 2, 2026

MobiGuide: Vocabulary-Guided Online Recovery for Mobile Manipulation

A central challenge in mobile manipulation is preserving multiple plausible action modes while remaining reactive during execution. For example, grasping a bottle in a cluttered scene can be achieved through multiple physically feasible base-arm motion plans. Deterministic VLA policies trained with L1L_1 regression are efficient, but they tend to average across valid modes and may produce physically invalid actions. Diffusion-based generative policies can better represent multimodal action distributions, yet their proposals may be weakly constrained when generated from pure noise, and iterative sampling can increase inference cost. Action chunking helps amortize inference, but it also introduces partially open-loop execution, allowing small mismatches to accumulate into drift. We present MobiGuide, a generative VLA model for efficient mobile manipulation, where action generation is guided by a structured trajectory vocabulary. Constructed from expert demonstrations, this vocabulary encodes plausible whole-body motion patterns and provides structured priors for feasible action generation. Given the current observation and instruction, MobiGuide locally refines these reference trajectories into context-compatible action proposals and selects the most suitable trajectory for execution. This preserves multimodal action candidates while keeping generation constrained around feasible expert-like motion modes. To mitigate chunk-induced drift, we further introduce a lightweight residual correction module that performs high-frequency, per-step adjustments during rollout. Across diverse mobile manipulation tasks, MobiGuide improves success and stability under execution disturbances while maintaining low-latency inference.

Jia Syuen Lim, Zhizhen Zhang, Peter Bohm +3

UQMM Lab, The University of Queensland, Brisbane, Australia · Robotics and Autonomous Systems Group, CSIRO, Brisbane, Australia

Mobile ManipulationDiffusion PolicyEfficient VLA Model InferenceRobustness of VLA Models
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Mar 24, 2026

SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation

Vision-Language-Action (VLA) models show promise for robotic control, yet performance in complex household environments remains sub-optimal. Mobile manipulation requires reasoning about global scene layout, fine-grained geometry, and high-dimensional continuous actions, making standard imitation learning insufficient. We introduce a framework for learning spatially-grounded VLA models that strengthens perception and representation through auxiliary task co-training and multi-modal input enhancement. Our method addresses the challenge of controlling a 13-dimensional action space involving coordinated base motion, arm articulation, and gripper actuation. To enrich spatial understanding, the model incorporates multi-view RGB observations, depth cues, and short temporal history, providing perspectives of both global scene structure and local manipulation context. To improve representation quality, we co-train auxiliary decoders that reconstruct interpretable intermediate signals - including global robot position, joint configurations, grasp affordances, target-object relative pose, and segmentation masks - from shared visual-language features. These objectives provide dense supervision that encourages the backbone to develop spatially grounded, manipulation-aware latent representations. Through extensive evaluation on home rearrangement tasks, our approach achieves consistent improvements across picking, placing, opening, and closing operations, substantially outperforming direct imitation learning. Our findings suggest that spatial grounding through auxiliary and multi-modal learning provides a strong direction for scaling VLA models toward general-purpose domestic robots.

Ruisen Tu, Arth Shukla, Sohyun Yoo +5

UC San Diego · Lambda, Inc.

Spatial Grounding for VLA ModelsVision-Language-Action ModelsMobile ManipulationMulti-Task Robotic Manipulation
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Mar 18, 2026

Rapid Adaptation of Particle Dynamics for Generalized Deformable Object Mobile Manipulation

We address the challenge of learning to manipulate deformable objects with unknown dynamics. In non-rigid objects, the dynamics parameters define how they react to interactions -- how they stretch, bend, compress, and move -- and they are critical to determining the optimal actions to perform a manipulation task successfully. In other robotic domains, such as legged locomotion and in-hand rigid object manipulation, state-of-the-art approaches can handle unknown dynamics using Rapid Motor Adaptation (RMA). Through a supervised procedure in simulation that encodes each rigid object's dynamics, such as mass and position, these approaches learn a policy that conditions actions on a vector of latent dynamic parameters inferred from sequences of state-actions. However, in deformable object manipulation, the object's dynamics not only includes its mass and position, but also how the shape of the object changes. Our key insight is that the recent ground-truth particle positions of a deformable object in simulation capture changes in the object's shape, making it possible to extend RMA to deformable object manipulation. This key insight allows us to develop RAPiD, a two-phase method that learns to perform real-robot deformable object mobile manipulation by: 1) learning a visuomotor policy conditioned on the object's dynamics embedding, which is encoded from the object's privileged information in simulation, such as its mass and ground-truth particle positions, and 2) learning to infer this embedding using non-privileged information instead, such as robot visual observations and actions, so that the learned policy can transfer to the real world. On a mobile manipulator with 22 degrees of freedom, RAPiD enables over 80%+ success rates across two vision-based deformable object mobile manipulation tasks in the real world, under various object dynamics, categories, and instances.

Bohan Wu, Roberto Martín-Martín, Li Fei-Fei

the University of Texas at Austin, USA

Deformable Object ManipulationGeneralization in Robotic ManipulationMobile ManipulationAdaptive ControlRobot Dynamics Learning
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Mar 17, 2026

Beyond Cybathlon: On-demand Quadrupedal Assistance for People with Limited Mobility

Background: Assistance robots have the potential to increase the independence of people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of attaching robotic arms to motorized wheelchairs offer limited additional mobility at the cost of increased size and reduced wheelchair maneuverability. Methods: We present an on-demand quadrupedal assistance robot system controlled via a shared autonomy approach, which combines semi-autonomous task execution with human teleoperation. Due to the mobile nature of the system it can assist the operator whenever needed and perform autonomous tasks independently, without otherwise restricting their mobility. We automate pick-and-place tasks, as well as robot movement through the environment with semantic, collision-aware navigation. For teleoperation, we present a mouth-level joystick interface that enables an operator with reduced mobility to control the robot's end effector for precision manipulation. Results: We showcase our system in the Cybathlon 2024 Assistance Robot Race, and validate it in an at-home experimental setup, where we measure task completion times and user satisfaction. We find our system capable of assisting in a broad variety of tasks, including those that require dexterous manipulation. The user study confirms the intuition that increased robot autonomy alleviates the operator's mental load. Conclusions: We present a flexible system that has the potential to help people in wheelchairs maintain independence in everyday life by enabling them to solve mobile manipulation problems without external support. We achieve results comparable to previous state-of-the-art on subjective metrics while allowing for more autonomy of the operator and greater agility for manipulation.

Carmen Scheidemann, Andrei Cramariuc, Changan Chen +2

ETH Zurich, Robotic Systems Lab, Zurich, Switzerland.

Assistive RoboticsHealthcare RoboticsMobile ManipulationRobot TeleoperationLegged RoboticsShared Autonomy
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Mar 16, 2026

AnolisVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation

In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based on visual observations and natural language instructions. This task is essential for service robots that operate in human environments, and requires safety, efficiency, and task-level generality. Although Vision-Language-Action models (VLAs) have demonstrated strong performance for this task, their deployment in resource-constrained environments remains challenging because of the computational cost of standard transformer backbones. To overcome this limitation, we propose AnolisVLA, a lightweight VLA that uses a deep state space model to process multimodal sequences efficiently. The model uses lightweight, fast sequential state modeling to process visual and textual inputs. This design enables efficient trajectory generation. We evaluated the proposed method in simulation and on two real robot platforms. Compared with SmolVLA at a comparable parameter scale, AnolisVLA achieved higher task success rates, faster inference, and comparable GPU memory usage. In mobile manipulation, it outperformed the larger, 3-billion-parameter π0.5π_{0.5} model by 21 percentage points in task success rate while achieving approximately three times faster inference.

Yusuke Takagi, Motonari Kambara, Daichi Yashima +3

Keio University, 3-14-1 Hiyoshi, Kohoku, Yokohama, Kanagawa 223-8522, Japan.

Language-Conditioned Robot ManipulationVision-Language-Action ModelsEfficient VLA Model InferenceMobile Manipulation
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Mar 10, 2026

Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Mobile manipulators are envisioned to serve more complex roles in people's everyday lives. With recent breakthroughs in large language models, task planners have become better at translating human verbal instructions into a sequence of tasks. However, there is still a need for a decision-making algorithm that can seamlessly interface with the high-level task planner to carry out the sequence of tasks efficiently. In this work, building on the idea of nonlinear lexicographic optimization, we propose a novel Hierarchical-Task Model Predictive Control framework that is able to complete sequential tasks with improved performance and reactivity by effectively leveraging the robot's redundancy. Compared to the state-of-the-art task-prioritized inverse kinematic control method, our approach has improved hierarchical trajectory tracking performance by 42% on average when facing task changes, robot singularity and reference variations. Compared to a typical single-task architecture, our proposed hierarchical task control architecture enables the robot to traverse a shorter path in task space and achieves an execution time 2.3 times faster when executing a sequence of delivery tasks. We demonstrated the results with real-world experiments on a 9 degrees of freedom mobile manipulator.

Xintong Du, Siqi Zhou, Angela P. Schoellig

Learning Systems and Robotics Lab (www.learnsyslab.org) at the Technical University of Munich, Germany, and the University of Toronto Institute for Aerospace Studies, Canada · University of Toronto Robotics Institute · Munich Institute of Robotics and Machine Intelligence (MIRMI) +1

Hierarchical Robot ControlMobile ManipulationModel Predictive ControlRobot Trajectory Tracking
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Mar 10, 2026

Perceptive Hierarchical-Task MPC for Sequential Mobile Manipulation in Unstructured Semi-Static Environments

As compared to typical mobile manipulation tasks, sequential mobile manipulation poses a unique challenge -- as the robot operates over extended periods, successful task completion is not solely dependent on consistent motion generation but also on the robot's awareness and adaptivity to changes in the operating environment. While existing motion planners can generate whole-body trajectories to complete sequential tasks, they typically assume that the environment remains static and rely on precomputed maps. This assumption often breaks down during long-term operations, where semi-static changes such as object removal, introduction, or shifts are common. In this work, we propose a novel perceptive hierarchical-task model predictive control (HTMPC) framework for efficient sequential mobile manipulation in unstructured, changing environments. To tackle the challenge, we leverage a Bayesian inference framework to explicitly model object-level changes and thereby maintain a temporally accurate representation of the 3D environment; this up-to-date representation is embedded in a lexicographic optimization framework to enable efficient execution of sequential tasks. We validate our perceptive HTMPC approach through both simulated and real-robot experiments. In contrast to baseline methods, our approach systematically accounts for moved and phantom obstacles, successfully completing sequential tasks with higher efficiency and reactivity, without relying on prior maps or external infrastructure.

Xintong Du, Jingxing Qian, Siqi Zhou +1

University of Toronto Institute for Aerospace Studies (UTIAS) and the Vector Institute for Artificial Intelligence, Canada. · School of Computing Science, Faculty of Applied Sciences, Simon Fraser University, Burnaby, BC, Canada. · Learning Systems and Robotics lab at the Technical University of Munich and the Munich Institute for Robotics and Machine Intelligence (MIRMI), Germany.

3D Scene Change DetectionMobile ManipulationRobot Task PlanningLong-Horizon Robotic ManipulationDynamic Robotic ManipulationModel Predictive Control
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Mar 10, 2026

Cutting the Cord: System Architecture for Low-Cost, GPU-Accelerated Bimanual Mobile Manipulation

We present a bimanual mobile manipulator built on the open-source XLeRobot with integrated onboard compute for less than $1300. Key contributions include: (1) optimized mechanical design maximizing stiffness-to-weight ratio, (2) a Tri-Bus power topology isolating compute from motor-induced voltage transients, and (3) embedded autonomy using NVIDIA Jetson Orin Nano for untethered operation. The platform enables teleoperation, autonomous SLAM navigation, and vision-based manipulation without external dependencies, providing a low-cost alternative for research and education in robotics and robot learning.

Artemis Shaw, Chen Liu, Justin Costa +5

RoboticsMobile ManipulationBimanual Robotic Manipulation
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Mar 9, 2026

MoMaStage: Skill-State Graph Guided Planning and Closed-Loop Execution for Long-Horizon Indoor Mobile Manipulation

Long-horizon indoor mobile manipulation (MoMa) requires robots to execute extended navigation-manipulation sequences whose feasibility depends on state changes induced by preceding skills. Vision-language models (VLMs) can decompose instructions into plausible skill sequences, but they do not reliably track such cumulative embodiment constraints or revise a plan when execution deviates from expectation. We present MoMaStage, a map-light framework for state-consistent planning and closed-loop execution in long-horizon indoor MoMa. MoMaStage couples a frozen VLM with robot execution through three mechanisms: (i) a hierarchical library of grounded, executable skills and a topology-only projection of a Skill-State Graph (SSG) that constrains the VLM's planning space; (ii) an SSG verifier that propagates scene-region and gripper-occupancy state to reject infeasible plans before execution; and (iii) an event-driven monitor that triggers graph-grounded repair only when an observed outcome invalidates the remaining plan. The SSG captures the compact embodiment state needed for skill sequencing without requiring a dense scene map, while geometric and contact-level conditions remain within the underlying controllers. Experiments in physics-rich simulation and on a real mobile manipulator show that MoMaStage improves planning validity and long-horizon execution survival over the evaluated baselines, while reducing latency and model token consumption.

Chenxu Li, Zixuan Chen, Yetao Li +5

All authors are with Nanjing University, China.

Long-Horizon Robotic ManipulationMobile ManipulationLarge Language Model-Based Robot PlanningRobot Task PlanningModel-Based Planning
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Mar 9, 2026

Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance

Offshore inspection and maintenance have increasingly been using legged robots for routine sensing, yet many useful interventions still require physical interaction with tools, containers, and task-relevant objects. Employing robots for these tasks can reduce operators' exposure in confined, elevated, or potentially explosive areas. This paper presents a language-guided grasping pipeline for a legged mobile manipulator operating under partial observation. An operator defines the target, the system grounds it in RGB with open-vocabulary detection and promptable segmentation, extracts an object-centric RGB-D point cloud, improves sparse geometry through depth compensation and point-cloud completion, and selects a 6-DoF grasp using collision, clearance, reachability, and approach constraints. The system is implemented on a quadruped robot with an arm and evaluated in two cluttered tabletop scenes motivated by small-object retrieval during inspection and maintenance. Across paired trials, the proposed pipeline achieved 9/10 successful grasps, compared with 3/10 for a view-dependent deployment baseline. In this controlled setting, object-centric completion and execution-aware selection reduced approach collisions and improved the reliability of language-guided grasping for supervised field manipulation.

Dilermando Almeida, Juliano Negri, Guilherme Lazzarini +5

Department of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Brazil · Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil · Graduate School of Information Sciences, Tohoku University, Sendai, Japan +1

Robotic GraspingLanguage-Conditioned Robot ManipulationMobile Manipulation
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Mar 6, 2026

SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation

Long-horizon mobile manipulation needs whole-body demonstrations, and collecting them is the bottleneck. Keeping the robot in the loop ties the dataset to robot uptime, while recording human motion without it instead requires a conversion step that decides the joint-space action the policy learns. SuperSuit is a single wearable that both teleoperates a wheeled bimanual robot and, with the robot absent, records demonstrations for it. Its arms are mechanically aligned to the robot's, so recorded joint motion needs no inverse kinematics and no retargeting; one body-worn tracker drives the torso and the planar base through a deadband that keeps posture micro-sway out of the base command. Both roles emit the same 22-D command over the torso, planar base, two 7-DoF arms, and two grippers, with arm targets expressed as forward-looking increments of the observed joint state. On three real-robot tasks this lets active demonstrations replace most robot-in-the-loop collection without changing policy quality. With a 10-episode robot-grounded seed held fixed, the remaining 100 episodes can be recorded without the robot, whereas active-only training falls well short. SuperSuit moves the limit on dataset size from the robot to the operator.

Tongqing Chen, Hang Wu, Jiasen Wang +1

Tsinghua University, Beijing, China. · TARS Robotics, Shanghai, China.

Mobile ManipulationRobotic Data CollectionRobot TeleoperationLong-Horizon Robotic ManipulationRobot Imitation Learning
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Mar 4, 2026

Risk-Aware Reinforcement Learning for Mobile Manipulation

For robots to successfully transition from lab settings to everyday environments, they must begin to reason about the risks associated with their actions and make informed, risk-aware decisions. This is particularly true for robots performing mobile manipulation tasks, which involve both interacting with and navigating within dynamic, unstructured spaces. However, existing whole-body controllers for mobile manipulators typically lack explicit mechanisms for risk-sensitive decision-making under uncertainty. To our knowledge, we are the first to (i) learn risk-aware visuomotor policies for mobile manipulation conditioned on egocentric depth observations with runtime-adjustable risk sensitivity, and (ii) show risk-aware behaviours can be transferred through Imitation Learning (IL) to a visuomotor policy conditioned on egocentric depth observations. Our method achieves this by first training a privileged teacher policy using Distributional Reinforcement Learning (DRL), with a risk-neutral distributional critic. Distortion risk-metrics are then applied to the critic's predicted return distribution to calculate risk-adjusted advantage estimates used in policy updates to achieve a range of risk-aware behaviours. We then distil teacher policies with IL to obtain risk-aware student policies conditioned on egocentric depth observations. We perform extensive evaluations demonstrating that our trained visuomotor policies exhibit risk-aware behaviour (specifically achieving better worst-case performance) while performing reactive whole-body motions in unmapped environments, leveraging live depth observations for perception.

Michael Groom, James Wilson, Nick Hawes +1

Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, United Kingdom · Dyson Institute of Engineering & Technology, Malmesbury, United Kingdom · Bristol Robotics Laboratory, School of Engineering, University of West England, Bristol, United Kingdom

Risk-Sensitive RLMobile ManipulationVisuomotor Policy LearningWhole-Body Robotic Manipulation
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Mar 4, 2026

RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots

Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lacks a reproducible, large-scale benchmark for systematic evaluation. To fill this gap, we present RoboCasa365, a comprehensive simulation benchmark for household mobile manipulation. Built on the RoboCasa platform, RoboCasa365 introduces 365 everyday tasks across 2,500 diverse kitchen environments, with over 600 hours of human demonstration data and over 1600 hours of synthetically generated demonstration data -- making it one of the most diverse and large-scale resources for studying generalist policies. RoboCasa365 is designed to support systematic evaluations for different problem settings, including multi-task learning, robot foundation model training, and lifelong learning. We conduct extensive experiments on this benchmark with state-of-the-art methods and analyze the impacts of task diversity, dataset scale, and environment variation on generalization. Our results provide new insights into what factors most strongly affect the performance of generalist robots and inform strategies for future progress in the field.

Soroush Nasiriany, Sepehr Nasiriany, Abhiram Maddukuri +1

The University of Texas at Austin · NVIDIA Research

Robot Manipulation BenchmarksMobile ManipulationGeneralization in Robotic ManipulationMulti-Task Robotic Manipulation
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Mar 3, 2026

HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free human demonstrations. We augment UMI interfaces with egocentric sensing to capture the global context required for mobile manipulation, enabling portable, robot-free, and scalable data collection. However, naively incorporating egocentric sensing introduces a larger human-to-robot embodiment gap in both observation and action spaces, making policy transfer difficult. We explicitly bridge this gap with a cross-embodiment hand-eye policy design, including an embodiment agnostic visual representation; a relaxed head action representation; and a whole-body controller that realizes hand-eye trajectories through coordinated whole-body motion under robot-specific physical constraints. Together, these enable long-horizon mobile manipulation tasks requiring bimanual and whole-body coordination, navigation, and active perception. Results are best viewed on: https://hommi-robot.github.io

Xiaomeng Xu, Jisang Park, Han Zhang +6

Stanford University · Toyota Research Institute

Mobile ManipulationWhole-Body Robotic ManipulationRobot Imitation LearningLong-Horizon Robotic ManipulationCross-Embodiment Robot LearningRobotic Data Collection
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Mar 1, 2026

MiniUGV2_2: A Compact UAV-Deployable Tracked Ground Vehicle with Manipulation Capabilities

Exploring and inspecting Hidden Spaces, defined as environments whose entrances are accessible only to aerial robots but remain unexplored due to geometric constraints, limited flight time, and communication loss, remains a major challenge. We present miniUGV2_2, a compact UAV-deployable tracked ground vehicle that extends UAV capabilities into confined environments. The system introduces dual articulated arms, integrated LiDAR and depth sensing, and modular electronics for enhanced autonomy. A novel tether module with an electro-permanent magnetic head enables safe deployment, retrieval, and optional detachment, thereby overcoming prior entanglement issues. Experiments demonstrate robust terrain navigation, self-righting, and manipulation of objects up to 3.5 kg, validating miniUGV2_2 as a versatile platform for hybrid aerial-ground robotics.

Durgakant Pushp, Swapnil Kalhapure, Shaekh Mohammad Shithil +1

Luddy School of Informatics, Computing, and Engineering at Indiana University, Bloomington, IN 47408, USA. · S. Kalhapure is an Independent Researcher at the time of work.

RoboticsMobile RoboticsMobile ManipulationAerial Robotics
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Feb 26, 2026

InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipulator and robust perception under dynamically changing viewpoints. However, existing approaches face two key challenges: strong coupling between base and arm actions complicates control optimization, and perceptual attention is often poorly allocated as viewpoints shift during mobile manipulation. We propose InCoM, an intent-driven perception and structured coordination framework for mobile manipulation. InCoM infers latent motion intent to dynamically reweight multi-scale perceptual features, enabling stage-adaptive allocation of perceptual attention. To support robust cross-modal perception, InCoM further incorporates a geometric-semantic affinity refinement mechanism that enhances multimodal correspondence. On the control side, we design a decoupled coordinated flow matching action decoder that explicitly models bidirectional base-arm interactions, alleviating optimization difficulties caused by control coupling. Under matched non-privileged observations and task-specific training data, InCoM improves mean success over DSPv2 by 33.0, 26.1, and 23.6 percentage points on SetTable, TidyHouse, and PrepareGroceries, respectively. Furthermore, its effectiveness is consistently validated in real-world mobile manipulation tasks, where InCoM maintains a superior success rate over existing baselines.

Jiahao Liu, Cui Wenbo, Zhongpu Xia +3

SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · Anyverse Dynamics

Active PerceptionMobile ManipulationRobotic Perception
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Feb 26, 2026

Rethinking the Practicality of Vision-language-action Model: A Comprehensive Benchmark and An Improved Baseline

Vision-Language-Action (VLA) models have emerged as a generalist robotic agent. However, existing VLAs are hindered by excessive parameter scales, prohibitive pre-training requirements, and limited applicability to diverse embodiments. To improve the practicality of VLAs, we propose a comprehensive benchmark and an improved baseline. First, we propose CEBench, a new benchmark spanning diverse embodiments in both simulation and the real world with consideration of domain randomization. We collect 14.4k simulated trajectories and 1.6k real-world expert-curated trajectories to support training on CEBench. Second, using CEBench as our testbed, we study three critical aspects of VLAs' practicality and offer several key findings. Informed by these findings, we introduce LLaVA-VLA, a lightweight yet powerful VLA designed for practical deployment on consumer-grade GPUs. Architecturally, it integrates a compact VLM backbone with multi-view perception, proprioceptive tokenization, and action chunking. To eliminate reliance on costly pre-training, LLaVA-VLA adopts a two-stage training paradigm including post-training and fine-tuning. Furthermore, LLaVA-VLA extends the action space to unify navigation and manipulation. Experiments across embodiments demonstrate the capabilities of generalization and versatility of LLaVA-VLA , while real-world mobile manipulation experiments establish it as the first end-to-end VLA model for mobile manipulation. We will open-source all datasets, codes, and checkpoints upon acceptance to foster reproducibility and future research.

Wenxuan Song, Jiayi Chen, Xiaoquan Sun +12

The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · Huazhong University of Science and Technology, Wuhan, China. · Westlake University, Hangzhou, China. +1

Vision-Language-Action ModelsEfficient VLA ModelsVLA Model TrainingMobile Manipulation
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Feb 20, 2026

EgoPush: Egocentric Multi-Object Rearrangement for Mobile Robots via Constrained Teacher Observability

Humans rearrange objects in cluttered environments using egocentric perception, actively moving to keep task-relevant spatial cues in view. Mobile robots have not matched this: rearrangement is usually built on a global pose estimate or a map, which is exactly what a robot carrying one camera lacks, while pushing keeps changing the scene it would have to be built from. We present EgoPush, which pushes objects into anchor-relative formations from onboard RGB-D alone, with no global localization, external tracking, or map at deployment, and transfers zero-shot to a TurtleBot in controlled and visually cluttered scenes. What makes this learnable turns out to be a property of the teacher rather than of the student: three privileged teachers trained with identical rewards, architecture, and hyperparameters all exceed 98%98\% success, yet their distilled egocentric students reach 0%0\%, 54.8%54.8\%, and 87.3%87.3\%, the only variable being the teacher's observation function. EgoPush therefore trains the teacher under egocentric observability constraints, restricting it to visibility-limited cues and revealing target references only when the anchor is centrally visible, so that its supervision is recoverable by a depth-based student distilled online. Making the teacher trainable in the first place needs two further pieces: a role-grouped object-centric interface shared by teacher and student, and stage-wise temporally decayed rewards for long-horizon credit assignment. Videos, the playable task, and code are available at https://ai4ce.github.io/EgoPush/.

Boyuan An, Zhexiong Wang, Yipeng Wang +4

New York University

Robotic ManipulationRobot LearningMobile ManipulationRobotic Object PushingPolicy DistillationZero-Shot Robotic Manipulation
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Feb 18, 2026

Articulated 3D Scene Graphs for Open-World Mobile Manipulation

Semantic scene understanding has enabled robots to reason and perform affordance-driven object interaction. Despite this, real-world manipulation remains fundamentally limited by the inability to predict how objects move and are articulated. Long-horizon mobile manipulation requires closing the gap between semantics, geometry, and kinematics. In this work, we present MoMa-SG, a novel framework for building semantic-kinematic 3D scene graphs of articulated scenes containing a myriad of interactable objects. Given RGB-D sequences containing multiple object articulations, we temporally segment object interactions and infer object motion using occlusion-robust point tracking. We lift point trajectories to 3D and estimate articulation models using a novel unified twist estimation formulation that robustly estimates revolute and prismatic joint parameters in a single optimization pass. Next, we associate objects with articulations and detect contained objects by reasoning over parent-child relations at identified opening states. We also introduce the Arti4D-Semantic dataset, which provides parent-child relation annotations between container and contained objects together with object axis annotations across 62 in-the-wild RGB-D sequences containing 600 object interactions and three distinct observation paradigms. We extensively evaluate the performance of MoMa-SG on two datasets and ablate key design choices of our approach. In addition, real-world experiments on both a quadruped and a mobile manipulator demonstrate robust manipulation of articulated objects in everyday home environments. We provide code and data at: https://momasg.cs.uni-freiburg.de.

Martin Büchner, Adrian Röfer, Tim Engelbracht +5

University of Freiburg · ETH Zürich · University of Bonn

3D SGGArticulated Object ManipulationMobile ManipulationArticulated Object ReconstructionLong-Horizon Robotic Manipulation
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