Robotic Object Pushing

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16 papers6 papers in the last four weeks, up 50% on the four weeks before. 0.0% of all new papers.

Oct 26, 2026

PushAround: Collaborative Path Clearing via Physics-Informed Hybrid Search

Large vehicles in cluttered environments are often blocked by movable obstacles, motivating mobile robot teams to clear traversable corridors. Navigation Among Movable Obstacles (NAMO) approaches often abstract obstacle displacements while neglecting robot dimensions, obstacle mass, contact geometry and forces, yielding physically infeasible plans. PushAround is a physics-informed hybrid search that couples geometric connectivity reasoning with short-horizon simulation to construct executable clearing plans. Repeated W-clearance connectivity graph (WCCG) queries expose frontier gaps requiring widening and rank them by estimated downstream costs. The search generates direct or recursive pushing candidates, selecting obstacles, directions, contacts and forces, and admits physics-evaluated candidates. Numerical experiments validate WCCG against standard connectivity checks and demonstrate scaling to denser clutter and teams of up to four robots, with robustness to simulated execution-side physical-parameter mismatch.

Zili Tang, Zongyuan Li, Zishao Qiao +1

Peking University, Beijing 100871, China: School of Advanced Manufacturing and Robotics · Peking University, Beijing 100871, China: College of Engineering

Motion Planning Multi-Robot Motion Planning Contact-Aware Motion Planning Robotic Object Pushing

Oct 20, 2026

SPAR: Search-trained Push Action Ranker for Efficient Navigation Among Movable Obstacles

Navigation Among Movable Obstacles (NAMO) requires a robot to rearrange objects that block access to its goal. For a pushing robot, progress may depend on a setup push that enables a later opening without itself clearing a passage. The number of candidate sequences can grow exponentially with sequence length, making physics-based search costly. We introduce the Search-trained Push Action Ranker (SPAR), which learns to prioritize push primitives from simulated search outcomes. Searches over sequences of up to two pushes per object provide supervision that distinguishes immediate openings from setup pushes. A transformer trained with regression and ranking losses scores primitives using contact tokens conditioned on scene geometry and reachability regions. The same model guides simulator evaluations in SPAR Search and selects actions for direct execution in SPAR Policy. Both modes solve successive keyhole problems that open access to neighboring free-space regions within a NAMO planner. Across successful runs on

300

simulated two-push keyhole problems, SPAR Search reduces median simulator calls by

7.0\times

, leading to a

3.8\times

reduction in planning time relative to Random Search. Without retraining, the same ranker guides search on

600

keyhole problems which have no solution within two pushes. At a depth limit of five pushes, SPAR Search achieves

50.7\%

success within

100

simulator calls, compared with

20.2\%

for Random Search. These gains carry over to NAMO planning: on hard simulated scenes, SPAR Search achieves

47\%

success within

100

simulator calls, compared with

21\%

for Random Search. In real-robot experiments, SPAR Policy succeeds in

37/40

trials without a physics-based digital twin, compared with

33/40

for SPAR Search and

27/40

for Random Search.

Dhruv Metha Ramesh, Tri-An Nguyen, Junshan Huang +3

Robotic Object Pushing Robot Motion Planning Robotic Manipulation Motion Planning

Oct 14, 2026

SOMA: Stack-of-Objects Manipulation using a Quadruped Robot

Executing heavy-duty physical manipulation in unpredictable environments remains a major challenge for quadrupedal robots lacking dedicated manipulators. This paper presents SOMA, a hierarchical reinforcement learning framework for whole-body non-prehensile manipulation of multi-object stacks using a quadruped robot. Unlike traditional methods restricted to two-dimensional planar pushing, SOMA integrates explicit robot base-height control, unlocking full three-dimensional pushing strategies. This capability enables the quadruped to manipulate complex payload stacks as a unified system while preventing relative sliding or tipping of upper layers. The architecture factorizes control into a high-level manipulation policy using a Simplified Structured State Space Sequence (S5) encoder to reason over long-horizon temporal history and predict bounded target tracking commands via Beta distributions, and a low-level locomotion policy that dynamically adapts leg stance configurations. In extensive simulation benchmarks, SOMA achieves a

90.3\%

success rate for single-object reposing and up to

90.1\%

for dynamic two-box stacks under stringent error margins (

0.05\text{ m}

position error,

1^\circ

orientation error). Finally, we validate zero-shot sim-to-real transfer on a Unitree Go1 quadruped robot across diverse payload masses and geometries, demonstrating active base-height adjustments to maintain traction and balance during heavy pushing tasks.

Stergios E. Bachoumas, Panagiotis Artemiadis

Non-Prehensile Robotic Manipulation Robotic Manipulation Robotic Object Pushing Whole-Body Robotic Manipulation Hierarchical RL

Oct 9, 2026

Belief-Overlap Partial Inversion: Object-Centric Flow Replanning for Humanoid Loco-Manipulation

People often learn how an unfamiliar object moves through a small exploratory push, using its response to guide their next motion. Inspired by this behavior, we study humanoids pushing carts whose mass and caster stiffness are initially unknown. Our object-centric framework closes online estimation, flow-based planning, and whole-body control into a loop, creating a tension: replacing the plan whenever the belief shifts disrupts the maneuver under way, while keeping it strands the robot on a path its evidence has ruled out. Belief-Overlap Partial Inversion (BOPI) scales revision to agreement, inverting the flow under the old belief only as far as the beliefs disagree before reconstructing under the new one. Across 18 simulated goals it lowers pooled final position and yaw error by roughly 33% and 47% against fixed-depth replanning, and on hardware it translates and turns a non-holonomic cart.

Chen Yu, Sudarshan S Harithas, Juan Borbon +2

Northwestern University, USA · Multiply Labs, USA

Robotic Object Pushing Humanoid Loco-Manipulation Robotic Manipulation Belief-Space Planning

Oct 8, 2026

Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning

Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions. Precision placement has been studied with manipulators on benchmarks such as Push-T, where an omnidirectional end effector can readily re-approach an object from any side. For a mobile robot, correcting the same placement errors may require wide repositioning maneuvers around the object. We formalize multi-object pushing with a mobile robot as a multi-target assignment problem at tight tolerances. We propose a diffusion planner with a causal factorization: each robot denoising step is followed by an object step conditioned on the current clean robot estimate. This factorization allows us to sample multiple object outcomes for a single robot plan, capturing the multi-modality of pushing. We also design a set of pushing-specific costs for mobile robots, used for both guidance and best-of-

N

sampling at inference. On Push-T, our planner attains the highest success rate of the methods we compare, while having the lowest rate of implausible object motion. Across three multi-box pushing environments resembling warehouse scenarios, it raises the fraction of boxes placed by

21.5

percentage points over an unguided baseline, and replans

1.8\times

faster than a planner that denoises the two trajectories together. Lastly, we deploy the guided planner on a TurtleBot 3 Burger with object shapes absent from the training data, where it places

85.0\%

of objects within tolerance against Diffusion Policy's

37.5\%

.

Lance Pharand, Megnath Ramesh, Mark Chiu +2

Electrical and Computer Engineering Department at the University of Waterloo, ON, Canada

Non-Prehensile Manipulation Robotic Object Pushing Mobile Robotics Diffusion-Based Planning

Oct 5, 2026

Learning Coordinated Visuomotor Box-Pushing from Solo Demonstrations

Multi-robot imitation learning, particularly in settings where visuomotor policies are deployed in a communication-free, onboard decentralised style, represents an attractive paradigm. However, its realisation remains insufficiently understood, largely due to the difficulty of collecting collective demonstrations, since a single operator cannot control many robots simultaneously. Meanwhile, unlike coupled collaborative manipulation, many coordinated tasks achieve system-wide efficiency primarily through minimising inter-robot interference. This structure motivates us to study whether data collected by a teleoperated single-robot can be leveraged for large-scale coordinated box-pushing as a testbed. We systematically investigate dataset creation strategies and lightweight policy architectures. In particular, experiments with up to 40 robots highlight the difficulty of acquiring effective coordination solely through passive observation of other operating robots, revealing a concrete bottleneck for multi-robot research.

Ryusei Matsumoto, Mei Kinjo, Yoko Sasaki +1

National Institute of Advanced Industrial Science and Technology (AIST), Japan · Institute of Science Tokyo, Japan · The University of Tokyo, Japan

Visuomotor Policy Learning Multi-Robot Coordination Robotic Manipulation Robot Imitation Learning Robotic Object Pushing

Oct 3, 2026

DNPush: Distributed Nonparallel Pushing Force Control of Multi-Robot Systems for Object Transportation

This letter proposes DNPush, a Distributed Nonparallel Pushing force controller for cooperative object transport without inter-robot communication. Each robot uses local force measurements, a preassigned force weight, and a locally expressed desired transport direction, without requiring an object model, contact locations, a CoM estimate, or moment arm magnitudes online. DNPush regulates nonparallel force magnitudes from local force direction misalignments through a reciprocal-sine law. Under the stated contact conditions, Lyapunov analysis with LaSalle's invariance principle proves convergence to a unique rotational equilibrium, where perpendicular force contributions cancel and the net torque from normal forces is zero. Object velocity converges to the desired transport direction, and suitable contact regions exist for convex polygons. Simulations isolate the reciprocal-sine scaling mechanism, five-robot coordination, and active rotational damping. Hardware experiments with soft tactile differential-drive robots achieve a velocity direction error of 0.61 deg +/- 0.79 deg over five trials with a fixed transport direction and demonstrate object transport through waypoints with three robots.

Duy Anh Nguyen, Pham Duy Hung, Trung Dung Ngo

More-Than-One Robotics Laboratory, Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, Canada · Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology, Hanoi, Vietnam

Multi-Robot Cargo Transportation Robotic Object Pushing Multi-Robot Systems Force-Controlled Robotic Manipulation Multi-Robot Coordination

Sep 22, 2026

MultiPush: Learning to Rearrange with Teams of Car-Like Pushers

We focus on the problem of rearranging multiple objects within a constrained workspace via pushing using a team of car-like robots. While the use of multiple robots offers the potential for more efficient execution, the need for conflict resolution and the kinematic constraints arising from physics, robot design, and the workspace boundary make this problem especially challenging. Our key insight is that by exploiting the structure introduced by the car-like kinematics of the domain, we could relax the problem into an ordered assignment of Dubins curves to robots. To this end, we introduce MultiPush, a reinforcement-learning based framework that jointly determines an efficient schedule of pushing tasks and their allocation to available robots by leveraging a constraint-aware traversability graph. Across extensive simulated trials with up to 14 objects and teams of two to four robots, MultiPush reduces the makespan by up to 16% compared to the baselines while requiring up to 2.9 times faster planning time. We demonstrate MultiPush on a real-world scenario involving the rearrangement of 12 objects by two and three robots (1/10-scale racecars) in a constrained space.

Jeeho Ahn, Christoforos Mavrogiannis

Robotics Department, University of Michigan, Ann Arbor, USA

Robotic Object Pushing Multi-Robot Systems Multi-Robot Motion Planning Multi-Robot Coordination Constrained Motion Planning Multi-Robot Task Allocation+1

Sep 20, 2026

Receding-Horizon Pushing with Composable Object-Centric Policies

Non-prehensile manipulation is practical for relocating large, heavy, or geometrically ungraspable objects. Yet, long-horizon pushing of arbitrarily-shaped 3D objects couples three problems: 1) where to push the object so as to approach the target pose, 2) whether each push is stable and reachable, 3) whether subsequent actions remain feasible. We present an object-centric pushing policy within a feedback-guided hierarchical framework. At the low level, a learning-based policy predicts contact actions from a pose- and scale-normalized point cloud, conditioned on a near single-step subgoal. A stability score is applied to evaluate the predicted contacts by a quasi-static sliding-versus-tipping analysis. At the high level, BIT

^*

first searches for an object path, and the next several subgoals are checked by contact prediction and robot motion planning for future feasibility. Failed motion plans, as feedback, change the local path costs and trigger re-planning. During execution, only the first feasible action is executed. In simulation, we evaluate 22 objects in six different scenes, upon which we also conduct comprehensive ablation studies. Results demonstrate that our method outperforms baselines with a clear margin and can reliably achieve long-horizon object pushing tasks under different situations. We also report quantitative real-robot experiments with a Franka arm and qualitative demonstrations with a mobile manipulator for large and heavy objects, with directly zero-shot sim-to-real transfer.

Zhiyi Yuan, Tianrun Hu, Anxing Xiao +3

School of Computing and Smart Systems Institute, National University of Singapore, Singapore · Shanghai Innovation Institute, Shanghai, China

Long-Horizon Robotic Manipulation Sequential Decision Making Robotic Manipulation Robot Motion Planning Robotic Object Pushing

Sep 17, 2026

MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption

Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distribution, that cannot be inferred from perception alone. Prior experience across objects can provide an initial estimate of unseen object dynamics, but this estimate remains uncertain and can degrade further during sim-to-real transfer. Adapting the dynamics through interaction can progressively refine the estimation, however, updating the model may invalidate the planned trajectory. Successful and efficient manipulation therefore requires both rapid dynamics adaptation and a planning strategy that can incorporate this evolution. In this work, we introduce MetaPusher, a meta-learning and adaptive planning framework for nonprehensile manipulation of unseen objects without prior object-specific interactions. A meta-learned dynamics model rapidly adapts from interactions during task execution, while an adaptive kinodynamic planner updates long-horizon plans by reusing and refining its existing search tree. This coupling enables manipulation and adaptation without a separate data collection phase. We evaluate MetaPusher on unseen objects in simulation and in sim-to-real scenarios, comparing against fine-tuning and active learning methods, MPPI-based control, and a reinforcement learning policy. It achieves lower prediction error and improves task success rate by up to 20%.

Donghyung Lee, Seyedali Golestaneh, Jaskrit Singh +3

Non-Prehensile Robotic Manipulation Robotic Manipulation Robot Learning Robotic Object Pushing Robot Dynamics Learning Long-Horizon Robotic Manipulation+1

Sep 2, 2026

MS-MEM: Multi-Skill Manipulation-Enhanced Mapping via Uncertainty- and Disturbance-Aware Action Selection

Accurate scene understanding in confined, cluttered spaces such as shelves is essential for service robots, as many everyday tasks require them to locate and retrieve objects reliably. Yet, it remains challenging due to severe occlusions, restricted accessibility, and the need to avoid excessive scene changes. In this paper, we propose Multi-Skill Manipulation-Enhanced Mapping (MS-MEM), an evidential framework for uncertainty-aware mapping that integrates active viewpoint selection, object pushing, and grasping. MS-MEM combines scene-level metric-semantic evidential belief estimators with an uncertainty-aware grasp representation. This representation is learned using a novel full-evidential grasp estimator that models both grasp affordance and orientation uncertainty. In our framework, candidate perception and manipulation actions are evaluated within a unified action selection pipeline using a common information gain criterion. For manipulation actions, we further introduce a collateral disturbance constraint (CDC) that discourages excessive changes to confident regions of the scene belief. This enables MS-MEM to select actions that effectively reduce map uncertainty while limiting collateral scene changes. Experimental results show that, compared with single-skill and unconstrained baselines that ignore scene disturbance, MS-MEM achieves higher mapping accuracy while substantially reducing scene disturbance, highlighting the synergistic effects of active viewpoint selection, push, and grasp actions.

Yitian Shi, Jesper Mücke, Nils Dengler +3

Karlsruhe Institute of Technology, Germany · University of Bonn, Germany · Technical University of Darmstadt, Germany +1

Robotic Object Pushing Semantic Mapping Robotic Manipulation Robotic Grasping

May 26, 2026

Resolving Conflicts Where and When They Arise: Reactive Composition of Multi-Goal Behavior

Multi-goal robotic tasks are commonly delegated to planning, because reactive control is prone to local minima when objectives conflict. We show that many such failures stem from static goal representations, not task complexity: the designer defines the objectives, but where and when they must be traded off follows from the world's interaction structure which changes with the state. To read this structure, we extend Active InterCONnect (AICON), a graph of recursive estimators coupled by differentiable interconnections, with adaptive nullspace projections: lower-priority gradients enter the nullspace of higher-priority ones wherever they meet along the graph, not only in action space, ordered by current gradient magnitudes, not a fixed hierarchy. Where two gradients oppose and no weighting of them makes progress, the system instead explores in the nullspace of the stronger one until the conflict dissolves. We robustly solve 100 non-convex navigation and 100 pushT problems, outperforming static potential fields, a diffusion policy, and the same method without projection. Unmodified but embedded in a larger graph, it absorbs perceptual uncertainty, joint limits, and self-collisions on a real robot, solving 49 of 50 pushing trials across varied objects, with active camera control and disturbance recovery emerging from the same coupling. Much of the behavior attributed to planning may thus be within reach of control, once goal representations compose from the conflicts they encounter.

Vito Mengers, Oliver Brock

Robotics and Biology Laboratory, Technische Universität Berlin · Science of Intelligence (SCIoI), Cluster of Excellence, Berlin, Germany · Robotics Institute Germany

Robotic Control Robot Navigation Robotic Object Pushing

May 2, 2026

VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids

The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material handling in warehouses. To robustly manipulate heavy objects to arbitrary goal configurations, the robot must cope with unknown object mass and ground friction, noisy onboard perception, and actuation errors; all in a real-time feedback loop. Existing solutions either rely on privileged object-state information without onboard perception or lack robustness to variations in goal configurations and object physical properties. In this work, we present VOFA, a visual goal-conditioned humanoid loco-manipulation system capable of pushing objects with unknown physical properties to arbitrary goal positions. VOFA consists of a two-level hierarchical architecture with a high-level visuomotor policy and a low-level force-adaptive whole-body controller. The high-level policy processes noisy onboard observations and generates goal-conditioned commands to operate in closed loop across diverse object-goal configurations, while the low-level whole-body controller provides robustness to variations in object physical properties. VOFA is extensively evaluated in both simulation and real-world experiments on the Booster T1 humanoid robot. Our results demonstrate strong performance, achieving over 90% success in simulation and over 80% success in real-world trials. Moreover, VOFA successfully pushes objects weighing up to 17kg, exceeding half of the Booster T1's body weight.

Zichao Hu, Zifan Xu, Dongsik Chang +6

Department of Computer Science, The University of Texas at Austin · Amazon Inc. · Sony AI

Humanoid Loco-Manipulation Whole-Body Control Robotic Manipulation Vision-Based Robot Control Robotic Object Pushing Adaptive Control

Mar 12, 2026

Push, Press, Slide: Mode-Aware Planar Contact Manipulation via Reduced-Order Models

Non-prehensile planar manipulation, including pushing and press-and-slide, is critical for diverse robotic tasks, but notoriously challenging due to hybrid contact mechanics, under-actuation, and asymmetric friction limits that traditionally necessitate computationally expensive iterative control. In this paper, we propose a mode-aware framework for planar manipulation with one or two robotic arms based on contact topology selection and reduced-order kinematic modeling. Our core insight is that complex wrench-twist limit surface mechanics can be abstracted into a discrete library of physically intuitive models. We systematically map various single-arm and bimanual contact topologies to simple non-holonomic formulations, e.g. unicycle for simplified press-and-slide motion. By anchoring trajectory generation to these reduced-order models, our framework computes the required object wrench and distributes feasible, friction-bounded contact forces via a direct algebraic allocator. We incorporate manipulator kinematics to ensure long-horizon feasibility and demonstrate our fast, optimization-free approach in simulation across diverse single-arm and bimanual manipulation tasks. Supplementary videos and additional information are available at: https://sites.google.com/view/pushpressslide

Melih Özcan, Umut Orguner, Ozgur S. Oguz

Dept. of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkiye. · Dept. of Computer Engineering, Bilkent University, Ankara, Turkiye.

Non-Prehensile Robotic Manipulation Robotic Manipulation Contact-Rich Robotic Manipulation Robotic Object Pushing Motion Planning Bimanual Robotic Manipulation+1

Feb 22, 2026

Visual Prompt Guided Unified Pushing Policy

As one of the simplest non-prehensile manipulation skills, pushing has been widely studied as an effective means to rearrange objects. Existing approaches, however, typically rely on multi-step push plans composed of pre-defined pushing primitives with limited application scopes, which restrict their efficiency and versatility across different scenarios. In this work, we propose a unified pushing policy that incorporates a lightweight prompting mechanism into a flow matching policy to guide the generation of reactive, multimodal pushing actions. The visual prompt can be specified by a high-level planner, enabling the reuse of the pushing policy across a wide range of planning problems. Experimental results demonstrate that the proposed unified pushing policy not only outperforms existing baselines but also effectively serves as a low-level primitive within a VLM-guided planning framework to solve table-cleaning tasks efficiently.

Hieu Bui, Ziyan Gao, Yuya Hosoda +1

Graduate School of Information Science and Engineering, Ritsumeikan University, Japan. · Japan Advanced Institute of Science and Technology (JAIST). · College of Information Science and Engineering, Ritsumeikan University, Japan.

Robotic Object Pushing Robotic Manipulation

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\%

success, yet their distilled egocentric students reach

0\%

,

54.8\%

, and

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 Manipulation Robot Learning Mobile Manipulation Robotic Object Pushing Policy Distillation Zero-Shot Robotic Manipulation