Contact-Aware Motion Planning

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13 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Oct 1, 2026

H-SPAR: Hydrodynamic-aware Simulation for Particle Transport and Autonomous Robots

Environmental robotic sampling requires considering the dual influence of water currents on robotic motion and particle transport. Existing marine robotics simulators generally model flow, autonomy, and sampling targets separately, limiting joint evaluation of mission cost and sampling performance. H-SPAR integrates spatially and temporally varying velocity fields, Lagrangian particle transport, probabilistic sampling, and ROS 2/Gazebo-based uncrewed surface vehicle (USV) autonomy. In this work, shared precomputed flow fields drive particle advection and current-induced forces during closed-loop vehicle execution. Path-planning experiments show that the existing current-aware planner SVF-RRT* achieves 69.4% lower upstream cost than conventional RRT* at the planning level, but this reduction falls to 41.7% during execution under time-varying currents, reflecting temporal flow variation, vehicle motion constraints, and path deviation omitted during planning. Coverage experiments show that sweep orientation changes the particle-sampling rate by up to 22.2% under the complete H-SPAR configuration. These findings highlight the importance of evaluating planning, vehicle execution, particle transport, and sampling together under consistent hydrodynamic conditions. The project webpage is available at https://sites.google.com/view/h-spar, and the open-source code is available on GitHub at https://github.com/naviiidz/h-spar-sim.

Navid Zarrabi, Nariman Yousefi, Sajad Saeedi

Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada. · Department of Chemical Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada. · Department of Computer Science, University College London, London, United Kingdom.

Contact-Aware Motion PlanningRobotic Data CollectionRobotics SimulationMotion PlanningSampling-Based Motion Planning
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Sep 22, 2026

Planning Trajectories that Bounce: Reflection Classes for Collision-Tolerant Robots

Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar navigation in environments where a mobile robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost path within each class. We show that, although a reflecting path cannot be shorter than the shortest collision-free path, it can reduce execution time and actuation effort by replacing costly changes in heading with controlled environmental interactions. The planned trajectories are executed using a contact-aware sampling-based controller with the robot's full dynamics. In our experiments, we demonstrate that in our simulated test scenario, the best reflecting class can reduce time and control effort. Our results show that controlled contact can provide dynamically advantageous navigation strategies that are excluded by conventional collision-avoidance formulations.

Subhadeep Koley, Subhrajit Bhattacharya, David Saldaña

Lehigh University, Bethlehem, PA 18015, USA

Contact-Aware Motion PlanningTrajectory GenerationRobot NavigationMotion PlanningRobot Motion Planning
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Sep 22, 2026

Relative Contact Velocity-Controlled Hand-Object Mechanism for Dexterous Tool Manipulation

This work investigates how to enable general multi-finger robotic hands to perform the complete tool manipulation process, which entails picking up a tool, loading it into a suitable pose, and then wielding it. Inspired by human tool manipulation and mechanical design principles, we model the hand and the tool as a unified hand-object mechanism (HOM) composed of sub-assemblies. Specifically, we define a HOM as consisting of the hand, the object, and the generalized contact frames, allowing the HOM's motions to be expressed with the same set of Cartesian-space relative contact velocities, irrespective of the hand's kinematics and geometry. Then, we define a HOM's sub-assemblies as relative contact velocity and contact force constraints between fingers. Building on these definitions, we developed a lightweight and physically interpretable motion planning and contact estimation framework using least squares and a complementary filter. We evaluated our framework in simulation by teleoperating five different robotic hands. The results show that our framework enabled all five hands to execute the complete tool manipulation process, achieving dexterous behaviors even from identical, simple reference trajectories. Furthermore, the results showcase our framework's adaptability to different hands, tools, and tasks, enabled by its kinematic and geometric foundation. Please visit website for videos: https://sunyuw.github.io/hom-sim/

Sunyu Wang, Jean Oh, Nancy S. Pollard

The authors are with the Robotics Institute at Carnegie Mellon University, Pittsburgh, USA.

Contact-Aware Motion PlanningDexterous ManipulationContact-Rich Robotic ManipulationGeneralization in Robotic ManipulationRobotic Tool UseMotion Planning
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Sep 18, 2026

Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation

Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions. Existing methods typically avoid broad evaluation of contact-mode sequences through search heuristics or optimization reformulations. We revisit broad evaluation in light of modern GPU hardware and introduce Contact-Mode Expansion with parallel Trajectory optimization (CoMET), which combines GPU-parallel trajectory evaluation with greedy contact-mode expansion. On planar pushing benchmarks, CoMET is competitive with optimization-based, sampling, and tree-search baselines in solution quality and planning time, matching the full-enumeration reference on nearly all instances with fewer evaluations and shorter planning times. Ablations suggest that much of the performance gain comes from the high-throughput trajectory evaluator. In bimanual nonprehensile manipulation, GPU-friendly local mode expansion achieves higher planning success than the tested adaptive tree search as the mode space grows. These results demonstrate that broad explicit mode evaluation provides a simple yet effective alternative for CRMP.

Jiayun Li, Georgia Chalvatzaki

PEARL Lab, Department of Computer Science, TU Darmstadt, Darmstadt, Germany · Hessian.AI, Darmstadt, Germany · Robotics Institute Germany (RIG), Germany

Contact-Aware Motion PlanningMotion PlanningContact-Rich Robotic Manipulation
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Aug 17, 2026

FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots

Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still depend heavily on manually designed gaits and environment-specific motion scripts. This work presents a planning framework for serial multi-segment soft robots with deformable body segments and boundary suction pads. The formulation targets full 3D navigation on complex surfaces and explicitly handles discrete adhesion switching and continuous body deformation under geometric, collision, and quasi-static feasibility constraints, while remaining agnostic to the specific actuation realization used to produce segment deformation. Its core, block-wise IK hybrid search (IKHS), performs best-first search over feasible adhesion transitions while solving inverse kinematics only on induced free blocks. On top of IKHS, primitive-augmented hybrid search (PaHS) uses a learned observation--primitive embedding to retrieve short validated motion segments for fast local proposal, with fallback to standard IKHS branching when retrieval fails. In simulation, the framework consistently outperforms controlled baselines in planning success, transition quality, and efficiency across diverse terrains. PaHS matches IKHS in success rate while substantially reducing planning time. Repeated hardware experiments on a pneumatic multi-segment soft robot further demonstrate executability and online recovery under actuation and adhesion uncertainty.

Zili Tang, Tiecheng Guo, Qinyue Zhang +1

School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China

Contact-Aware Motion PlanningMotion PlanningRobot Motion PlanningSoft RoboticsConstrained Motion Planning
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Aug 16, 2026

Contact Modes Are Strata: What Geometric Structure Buys in Discrete-Continuous Planning

Contact-rich manipulation poses a discrete question and a continuous one at once, namely which contacts are active and how to move while they hold. The two are coupled by a change of dimension, since each contact that a robot maintains confines its motion to a lower-dimensional manifold. We make that coupling the explicit object of planning by observing that a contact mode is not merely analogous to a stratum of the configuration space; it is one. A plan is then a walk over strata whose within-stratum segments are geodesics. On two contact-rich manipulation tasks in simulation, pushing a T-shaped block around obstacles and reorienting a cube in a dexterous hand, our planner returns solutions within seconds with no mode, contact sequence, or stratum given in advance.

Phone Thiha Kyaw, Jonathan Kelly

Space and Terrestrial Autonomous Robotic Systems (STARS) Laboratory University of Toronto Institute for Aerospace Studies (UTIAS) Toronto, Ontario M3H 5T6, Canada

Constrained Motion PlanningContact-Rich Robotic ManipulationMotion PlanningContact-Aware Motion Planning
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Jul 27, 2026

Motion Generation With Environmental Constraints

Robot motion planning faces challenges in high-dimensional spaces and uncertain environments, often constrained by the need for collision-free motions. We advocate an alternative approach, Environmental Constraint Exploitation (ECE), where deliberate contact with the environment simplifies planning by reducing dimensionality and computational complexity. By integrating ECE into motion planning algorithms, we bias exploration to task-relevant regions and leverage contact for uncertainty reduction to improve robustness during execution. We evaluate ECE benefits with RRT-based planners and demonstrate their practical benefits in a real-world application. This work consolidates and extends prior research, showcasing how ECE simplifies motion planning while enhancing adaptability and performance in complex environments.

Előd Páll, Oliver Brock

Robotics and Biology Laboratory, Technische Universität Berlin, Germany · Science of Intelligence, Research Cluster of Excellence, Berlin, Germany

Constrained Motion PlanningContact-Aware Motion PlanningMotion PlanningRobot Motion PlanningSampling-Based Motion Planning
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Jul 20, 2026

FARO: Feasibility-Aware Robot Motion Optimization

Fast planning of novel behaviors in unseen scenarios remains a fundamental challenge in robotics. The high-dimensional, hybrid, and underactuated nature of humanoid loco-manipulation continues to hinder the realization of this goal. In this paper, we address this challenge by proposing a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence. By integrating this module with a feasibility-guided tree search and a Large Language Model (LLM)-based contact plan sampling strategy, we demonstrate that the proposed framework can substantially improve the search process. Furthermore, we show that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and show that the resulting trajectories are of sufficiently high quality for execution in real-world loco-manipulation scenarios. A supplementary video is available at: https://youtu.be/R6qCHoCormQ.

Michal Ciebielski, Shafeef Omar, Aaron Johnson +1

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany. · Institute for Advanced Study, Technical University of Munich, Garching, Germany. · Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

Humanoid Loco-ManipulationKinodynamic Motion PlanningTrajectory GenerationRobot Motion GenerationRobotic OptimizationContact-Aware Motion Planning
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Jun 28, 2026

SPACE: Swarm Pheromone Fields for Adaptive Collision-Aware Exploration

Massive robot swarms can explore unknown environments quickly, but adding robots eventually stops helping. Doorways and dense traffic create congestion, increasing inter-robot contacts and reducing the value of each additional robot. We study this safety-efficiency tradeoff for ground swarms of tens to hundreds of robots. We present SPACE, Swarm Pheromone Fields for Adaptive Collision-Aware Exploration. Inspired by ant foraging, SPACE maintains a shared environmental field with an attractive frontier pheromone, a repellent explore pheromone, and a fast robot-density field. Robots use local field guidance on a synchronized shared map. We evaluate SPACE on real building floorplans, namely sixteen home layouts from the HouseExpo dataset and eight campus floors from the KTH dataset, with swarms of up to two hundred and fifty-six robots. At congested swarm sizes, SPACE attains the lowest inter-robot contact rate on HouseExpo and lies on the empirical Pareto frontier on KTH, together with handed lane formation at sizes where Lane has fewer contacts. Relative to a greedy nearest-frontier planner, SPACE reduces the contact rate by four to seventeen times while keeping coverage time within about two percent. The results indicate that, at this scale, coordination mainly improves safety rather than coverage time.

Haohua Que, Haojia Gao, Mingkai Liu +3

Department of Electronic Engineering, Tsinghua University, Beijing, China. · Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China. · School of Software and Microelectronics, Peking University, Beijing, China.

Robot Collision AvoidanceSwarm RoboticsMulti-Robot ExplorationMulti-Robot CoordinationRobot SafetyFrontier-Based Exploration+2
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Apr 30, 2026

Task-Conditioned Uncertainty Costmaps for Legged Locomotion

Legged robots maintain dynamic feasibility through multicontact interactions with terrain. Learned foothold prediction can provide feasibility-aware costs for motion planning and path selection, but accurately predicting future contacts from perceptual inputs such as height scans remains challenging on highly unstructured terrain, even with a repetitive gait cycle. In this work, we show that modeling epistemic uncertainty in predicted footholds, conditioned on terrain observations and commanded motion, distinguishes in-distribution from out-of-distribution operating regimes in simulation and real-world settings. This allows a single learned model, trained on limited data distributions, to express uncertainty caused by missing training coverage. We use this learned uncertainty to detect OOD regions and incorporate them into a unified costmap-generation framework for uncertainty-aware path planning. Using these uncertainty-aware costmaps, we evaluate feasibility error across in-distribution and OOD terrains in simulation and real-world settings. The results show improved OOD detection, up to a 37% reduction in simulation feasibility error, and more reliable planning behavior than geometry-only baselines.

Kartikeya Singh, Christo Aluckal, Romeo Orsolino +1

DRONES Lab, University at Buffalo, NY, USA · Ocado Technology, UK

Robot LocomotionEpistemic UncertaintyTerrain-Aware Robot LocomotionLegged RoboticsOOD DetectionRobot Motion Planning+1
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Apr 17, 2026

Contact-Aware Planning and Control of Continuum Robots in Highly Constrained Environments

Continuum robots are well suited for navigating confined and fragile environments, such as vascular or endoluminal anatomy, where contact with surrounding structures is often unavoidable. While controlled contact can assist motion, unfavorable contact can degrade controllability, induce kinematic singularities, or introduce safety risks. We present a contact-aware planning approach that evaluates contact quality, penalizing hazardous interactions, while permitting benign contact. The planner produces kinematically feasible trajectories and contact-aware Jacobians which can be used for closed-loop control in hardware experiments. We validate the approach by testing the integrated system (planning, control, and mechanical design) on anatomical models from patient scans. The planner generates effective plans for three common anatomical environments, and, in all hardware trials, the continuum robot was able to reach the target while avoiding dangerous tip contact (100% success). Mean tracking errors were 1.9 +/- 0.5 mm, 1.2 +/- 0.1 mm, and 1.7 +/- 0.2 mm across the three different environments. Ablation studies showed that penalizing end-of-continuum-segment (ECS) contact improved manipulability and prevented hardware failures. Overall, this work enables reliable, contact-aware navigation in highly constrained environments.

Aedan Mangan, Kehan Long, Ki Myung Brian Lee +3

Department of Mechanical and Aerospace Engineering, University of California San Diego, San Diego, CA 92093, USA · SceniX Inc, New York, NY 10011, USA · Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA +1

Constrained Motion PlanningContact-Aware Motion PlanningContinuum Robot ControlMotion PlanningRobot Motion PlanningSurgical Robotics
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Mar 5, 2026

CT-Enabled Patient-Specific Simulation and Contact-Aware Robotic Planning for Cochlear Implantation

Robotic cochlear-implant (CI) insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma and prevent failure modes such as locking and buckling. Aligned with the integration of advanced medical imaging and robotics for autonomous, precision interventions, this paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation. We develop a low-dimensional, differentiable Cosserat-rod model of the electrode array coupled with frictional contact and pseudo-dynamics regularization to ensure continuous stick-slip transitions. Patient-specific cochlear anatomy is reconstructed from CT imaging and encoded via an analytic parametrization of the scala-tympani lumen, enabling efficient and differentiable contact queries through closest-point projection. Based on a differentiated equilibrium-constraint formulation, we derive an online direction-update law under an RCM-like constraint that suppresses lateral insertion forces while maintaining axial advancement. Simulations and benchtop experiments validate deformation and force trends, demonstrating reduced locking/buckling risk and improved insertion depth. The study highlights how CT-based imaging enhances modeling, planning, and safety capabilities in robot-assisted inner-ear procedures.

Lingxiao Xun, Gang Zheng, Alexandre Kruszewski +1

Contact-Aware Motion PlanningRobotic InsertionSurgical RoboticsRobot Motion Planning
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Mar 5, 2026

Direct Contact-Tolerant Motion Planning With Vision Language Models

Navigation in cluttered environments often requires robots to tolerate contact with movable or deformable objects to maintain efficiency. Existing contact-tolerant motion planning (CTMP) methods rely on indirect spatial representations (e.g., prebuilt map, obstacle set), resulting in inaccuracies and a lack of adaptiveness to environmental uncertainties. To address this issue, we propose a direct contact-tolerant (DCT) planner, which integrates vision-language models (VLMs) into direct point perception and navigation, including two key components. The first one is VLM point cloud partitioner (VPP), which performs contact-tolerance reasoning in image space using VLM, caches inference masks, propagates them across frames using odometry, and projects them onto the current scan to generate a contact-aware point cloud. The second innovation is VPP guided navigation (VGN), which formulates CTMP as a perception-to-control optimization problem under direct contact-aware point cloud constraints, which is further solved by a specialized deep neural network (DNN). We implement DCT in Isaac Sim and a real car-like robot, demonstrating that DCT achieves robust and efficient navigation in cluttered environments with movable obstacles, outperforming representative baselines across diverse metrics. The code is available at: https://github.com/ChrisLeeUM/DCT.

He Li, Jian Sun, Chengyang Li +4

State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau, Macau, China. · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China. · Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences, Shenzhen, China.

Contact-Aware Motion PlanningRobot NavigationMotion PlanningRobot Motion Planning
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