Continuum Robot Modeling with Action Conditioned Flow Matching

• URL: http://arxiv.org/abs/2605.09216v1
• Date: Sat, 09 May 2026 23:22:49 GMT
• Title: Continuum Robot Modeling with Action Conditioned Flow Matching
• Abstract summary: Predicting the shape of tendon driven robots (TDCRs) at steady state from actuation remains challenging.<n>We present a lightweight 3D printed TDCR hardware platform and an RGB-D data collection pipeline with multiple cameras.<n>We learn a point cloud flow matching model that maps motor actuation states to the robot's settled 3D geometry.
• Score: 5.861206243996455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex tendon routing, compliance, friction, and fabrication variability. In this paper, we address this problem as kinematic self modeling conditioned on action. We present a lightweight 3D printed TDCR hardware platform and an RGB-D data collection pipeline with multiple cameras, and we learn a point cloud flow matching model that maps motor actuation states to the robot's settled 3D geometry. The model is trained from randomly sampled quasi static configurations and evaluated on test motor commands within the same TDCR design family and actuation range. We compare against prior 3D deformable object and robot self modeling approaches in both MuJoCo simulation and real hardware experiments. Experiments on simulated 2-, 3-, and 5-module TDCRs and real 2- and 3-module robots show improved shape prediction accuracy under CD and EMD metrics. We further show in simulation that the same conditional formulation generalizes to tip payload as a conditioning input, enabling payload conditioned steady-state shape prediction. These results demonstrate a data driven self modeling framework for quasi static TDCR geometry prediction.

Related papers

• KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding [58.541098216700796]
  evaluating the physical consistency of embodied world models is a critical open challenge.<n>Existing frameworks almost exclusively rely on Inverse Dynamics Models(IDMs) for action extraction.<n>We present KineBench, an IDM-free closed-loop benchmark for EWMs.
  arXiv  Detail & Related papers  (2026-07-22T08:04:17Z)
• Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement [16.29450701834732]
  We use a physical simulator as a backend rollout engine to evaluate candidate trajectories in parallel and refine nominal actions online.<n>We develop a real-to-sim module, trained purely on synthetic data, that maps a single RGB observation to simulation-compatible cloth state.<n>Real-robot experiments show higher success rates and stronger than baseline methods.
  arXiv  Detail & Related papers  (2026-06-23T13:20:12Z)
• PC2Model: ISPRS benchmark on 3D point cloud to model registration [1.0775419935941009]
  This paper introduces the PC2Model benchmark, a dataset designed to support the training and evaluation of both classical and data-driven methods.<n>Simulated data provide precise ground truth and controlled conditions, while real-world data introduce sensor and environmental artefacts.<n>This design supports robust training and evaluation across domains and enables the systematic analysis of model transferability from simulated to real-world scenarios.
  arXiv  Detail & Related papers  (2026-04-21T15:44:40Z)
• Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation [63.01022057888141]
  Asset Harvester is an image-to-3D model and end-to-end pipeline that converts sparse, in-thewild object observations from real driving logs into complete, simulation-ready assets.<n>SparseViewDiT is explicitly designed to address limited-angle views and other real-world data challenges.
  arXiv  Detail & Related papers  (2026-04-20T16:20:57Z)
• DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation [7.092954179436702]
  We present DART, a novel dual-arm framework for non-prehensile manipulation of objects on a tray.<n>Our pipeline is validated in simulation with objects of varying mass, geometry, and friction coefficients.<n>To the best of our knowledge, DART constitutes the first framework for non-prehensile dual-arm manipulation of objects on a tray.
  arXiv  Detail & Related papers  (2026-04-20T05:37:03Z)
• URDF-Anything+: Autoregressive Articulated 3D Models Generation for Physical Simulation [45.4820195450296]
  We present an end-to-end autoregressive framework that directly generates executable articulated object models from visual observations.<n>Building on this capability, we enable a new Real-Follow-Sim paradigm, where high-fidelity digital twins constructed from visual observations allow policies trained in simulation to transfer to real robots without online adaptation.
  arXiv  Detail & Related papers  (2026-03-14T16:23:44Z)
• Co-Design of Rover Wheels and Control using Bayesian Optimization and Rover-Terrain Simulations [0.3131740922192114]
  This paper presents a framework that co-designs rover wheel geometry and steering controller parameters using high-fidelity, full-vehicle simulations on deformable terrain.<n>We compare two strategies: simultaneous co-optimization of wheel and controller parameters versus a sequential approach that decouples mechanical and control design.<n>Across 3,000 full-vehicle simulations, campaigns finish in five to nine days, versus months with the group's earlier DEM-based workflow.
  arXiv  Detail & Related papers  (2026-02-02T02:10:34Z)
• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control [50.67481583744243]
  We introduce GeoDrive, which explicitly integrates robust 3D geometry conditions into driving world models.<n>We propose a dynamic editing module during training to enhance the renderings by editing the positions of the vehicles.<n>Our method significantly outperforms existing models in both action accuracy and 3D spatial awareness.
  arXiv  Detail & Related papers  (2025-05-28T14:46:51Z)
• Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model [63.336123527432136]
  We introduce Bench2Drive-R, a generative framework that enables reactive closed-loop evaluation.<n>Unlike existing video generative models for autonomous driving, the proposed designs are tailored for interactive simulation.<n>We compare the generation quality of Bench2Drive-R with existing generative models and achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-11T06:35:18Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)

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.