Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation

• URL: http://arxiv.org/abs/2605.30778v1
• Date: Fri, 29 May 2026 03:10:44 GMT
• Title: Object-Informed Model Predictive Path Integral Control for Non-Prehensile Robot Manipulation
• Abstract summary: Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions.<n>We propose a hierarchical formulation of model predictive path integral (MPPI) control that guides robot-level planning with a separately computed object-level plan.<n>We evaluate our method in both simulation and hardware using a 6-DoF xArm6 manipulator to perform object pushing tasks.
• Score: 7.6557550594212715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchical formulation of model predictive path integral (MPPI) control that guides robot-level planning with a separately computed object-level plan to achieve efficient long-horizon prediction. We first solve a simplified object-only problem, assuming the object can be actuated directly, and use the planned object trajectory as a reference in solving the joint robot-object planning problem. We evaluate our method in both simulation and hardware using a 6-DoF xArm6 manipulator to perform object pushing tasks in which the target object must reach a goal while avoiding static obstacles, necessitating non-myopic reasoning. Our object-informed MPPI increases task success by 40\% with a 26\% faster control frequency in simulation, and by 20\% in real experiments with similar computation as regular MPPI.

Related papers

• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
  Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
  arXiv  Detail & Related papers  (2026-04-22T22:58:47Z)
• ParaMaP: Parallel Mapping and Collision-free Motion Planning for Reactive Robot Manipulation [17.260700135265267]
  Real-time and collision-free motion planning remains challenging for robotic manipulation in unknown environments.<n>We propose a parallel mapping and motion planning framework that tightly integrates Euclidean Distance Transform (EDT)-based environment representation with a sampling-based model predictive control (SMPC) planner.<n>The effectiveness of the proposed framework is validated through extensive simulations and real-world experiments on a 7-DoF robotic manipulator.
  arXiv  Detail & Related papers  (2025-12-27T12:24:10Z)
• Cross-Entropy Optimization of Physically Grounded Task and Motion Plans [15.999074191893657]
  Previous TAMP algorithms have focused mainly on computational performance, completeness, or optimality.<n>We use a GPU-parallelized physics simulator to compute realizations of plans with motion controllers.<n>We demonstrate our approach for a set of tasks where the robot is able to exploit the environment's geometry to move an object.
  arXiv  Detail & Related papers  (2025-12-12T13:59:23Z)
• Constraint-Preserving Data Generation for Visuomotor Policy Learning [46.576295441652405]
  We use a single expert trajectory to generate robot demonstrations containing novel object geometries and poses.<n>These generated demonstrations are used to train closed-loop visuomotor policies that transfer zero-shot to the real world.
  arXiv  Detail & Related papers  (2025-08-05T22:20:02Z)
• REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation [57.628771707989166]
  We propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution.<n>ReMAC incorporates two key modules: a self-reflection module performing pre-conditions and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning.
  arXiv  Detail & Related papers  (2025-03-28T03:51:40Z)
• AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation [50.737355245505334]
  We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks. The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation.
  arXiv  Detail & Related papers  (2023-05-30T09:54:20Z)
• Active Exploration for Robotic Manipulation [40.39182660794481]
  This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method.
  arXiv  Detail & Related papers  (2022-10-23T18:07:51Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects [25.428781562909606]
  We present a framework for solving long-horizon planning problems involving manipulation of rigid objects. Our method plans in the space of object subgoals and frees the planner from reasoning about robot-object interaction dynamics.
  arXiv  Detail & Related papers  (2020-11-16T18:59:33Z)
• iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots [46.13039152809055]
  We present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines.
  arXiv  Detail & Related papers  (2020-04-18T17:46:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.