Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction

• URL: http://arxiv.org/abs/2505.13889v1
• Date: Tue, 20 May 2025 03:50:29 GMT
• Title: Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction
• Abstract summary: We propose a certifiably safe motion planning and framework for DLO manipulation.<n>At the core of our method is a predictive model that jointly estimates the DLO's future shape and tension.<n>Compared to state-of-theart methods, our approach achieves a higher task success rate while avoiding all safety violations.
• Score: 0.11510009152620666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulating deformable linear objects (DLOs) is challenging due to their complex dynamics and the need for safe interaction in contact-rich environments. Most existing models focus on shape prediction alone and fail to account for contact and tension constraints, which can lead to damage to both the DLO and the robot. In this work, we propose a certifiably safe motion planning and control framework for DLO manipulation. At the core of our method is a predictive model that jointly estimates the DLO's future shape and tension. These predictions are integrated into a real-time trajectory optimizer based on polynomial zonotopes, allowing us to enforce safety constraints throughout the execution. We evaluate our framework on a simulated wire harness assembly task using a 7-DOF robotic arm. Compared to state-of-the-art methods, our approach achieves a higher task success rate while avoiding all safety violations. The results demonstrate that our method enables robust and safe DLO manipulation in contact-rich environments.

Related papers

• Safe Reinforcement Learning using Ideas from Model Predictive Control [0.40831821256838124]
  Reinforcement learning enables the synthesis of control policies directly from data.<n>A persistent challenge is ensuring strict, hard safety constraints during the active learning phase.<n>We propose a framework that combines the adaptive, high-performance nature of deep reinforcement learning with the formal safety guarantees of model predictive control.
  arXiv  Detail & Related papers  (2026-07-08T10:35:04Z)
• World Engine: Towards the Era of Post-Training for Autonomous Driving [63.61277741443647]
  We introduce World Engine, a generative framework that reconstructs high-fidelity interactive environments from real-world logs.<n>World Engine substantially reduces failures in rare safety-critical scenarios and yields significantly larger gains than scaling pre-training data alone.<n>When deployed on a production-scale autonomous driving system, the resulting policy reduces simulated collisions.
  arXiv  Detail & Related papers  (2026-06-18T06:28:33Z)
• Pixels to Proofs: Probabilistically-Safe Latent World Model Control via Parallel Conformal Robust MPC [7.137568417435808]
  We present SLS2, a framework for safe feedback motion planning from pixels using robust model predictive control (MPC)<n>Our approach trains an action-conditioned joint-embedding world model with compact Markovian latent states.<n>To enforce safety for the true system despite imperfect latent predictions, we inform a GPU-accelerated system level synthesis (SLS) robust MPC scheme.
  arXiv  Detail & Related papers  (2026-06-14T04:34:03Z)
• Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation [52.122731171289665]
  We introduce the first framework for cross-model safety steering.<n>Our pipeline never accesses unsafe data on the target side.<n>We evaluate our approach in text-to-image and text-to-video generation across diverse source-target model pairs.
  arXiv  Detail & Related papers  (2026-06-03T18:00:04Z)
• Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic [3.071933369858584]
  We introduce STRELGen, a framework for the targeted generation of safety-critical driving scenarios.<n>Monitoring satisfaction levels of these specifications is differentiable, enabling gradient-based search.<n> STRELGen thus provides a flexible, interpretable, and powerful tool for stress-testing autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-18T19:00:14Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
  We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
  arXiv  Detail & Related papers  (2026-05-05T04:49:38Z)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models [75.214287449744]
  We introduce a framework for post-training policy refinement built around an Impartial World Model.<n>Our primary contribution is to teach this model to be honest about danger.<n>We demonstrate through extensive experiments, that our model significantly outperforms baselines in predicting failures.
  arXiv  Detail & Related papers  (2025-11-25T13:57:24Z)
• Building a Foundational Guardrail for General Agentic Systems via Synthetic Data [76.18834864749606]
  LLM agents can plan multi-step tasks, intervening at the planning stage-before any action is executed-is often the safest way to prevent harm.<n>Existing guardrails mostly operate post-execution, which is difficult to scale and leaves little room for controllable supervision at the plan level.<n>We introduce AuraGen, a controllable engine that synthesizes benign trajectories, injects category-labeled risks with difficulty, and filters outputs via an automated reward model.
  arXiv  Detail & Related papers  (2025-10-10T18:42:32Z)
• Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving [55.13109926181247]
  We introduce ReflectDrive, a learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion.<n>Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient.<n>Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors.
  arXiv  Detail & Related papers  (2025-09-24T13:35:15Z)
• Constrained Decoding for Robotics Foundation Models [12.916330118607918]
  We introduce SafeDec, a constrained decoding framework for autoregressive robot foundation models.<n>Task-specific safety rules are expressed as Signal Temporal Logic (STL) formulas and are enforced at inference time with minimal overhead.<n>Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining.
  arXiv  Detail & Related papers  (2025-09-01T19:17:40Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis [0.6749750044497732]
  This paper introduces a safety assurance framework for Large Language Models (LLMs)-controlled robots based on data-driven reachability analysis.<n>Our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models.
  arXiv  Detail & Related papers  (2025-03-05T21:23:15Z)
• DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time [10.491351627036707]
  Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT)<n>This paper presents Differentiable discrete branched Elastic rods for modeling Furcated DLOs in real-Time (DEFT)
  arXiv  Detail & Related papers  (2025-02-20T20:46:09Z)
• RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning [54.52545900359868]
  We propose RAD, a 3DGS-based closed-loop Reinforcement Learning framework for end-to-end Autonomous Driving.<n>To enhance safety, we design specialized rewards to guide the policy in effectively responding to safety-critical events and understanding real-world causal relationships.<n>Compared to IL-based methods, RAD achieves stronger performance in most closed-loop metrics, particularly exhibiting a 3x lower collision rate.
  arXiv  Detail & Related papers  (2025-02-18T18:59:21Z)
• Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis [6.267574471145217]
  Hamilton-Jacobi (H) reachability is a rigorous framework that enables robots to simultaneously detect unsafe states and generate actions.<n>We propose La Safety Filters, a latent-space reachability that operates directly on raw observation data.
  arXiv  Detail & Related papers  (2025-02-02T22:00:20Z)
• What Makes and Breaks Safety Fine-tuning? A Mechanistic Study [64.9691741899956]
  Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. We design a synthetic data generation framework that captures salient aspects of an unsafe input. Using this, we investigate three well-known safety fine-tuning methods.
  arXiv  Detail & Related papers  (2024-07-14T16:12:57Z)
• Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications [73.58451824894568]
  The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states. In safety-critical domains, such behaviors could lead to disastrous outcomes. We propose Objective Suppression, a novel method that adaptively suppresses the task reward maximizing objectives according to a safety critic.
  arXiv  Detail & Related papers  (2024-02-23T23:22:06Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects [7.72979328949568]
  We present DexDLO, a model-free framework that learns dexterous dynamic manipulation policies for deformable linear objects with a fixed-base dexterous hand. We show that our framework can efficiently learn five different DLO manipulation tasks with the same framework parameters.
  arXiv  Detail & Related papers  (2023-12-23T09:26:20Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach [2.741266294612776]
  We propose a model-free safety specification method that learns the maximal probability of safe operation. Our approach constructs a Lyapunov function with respect to a safe policy to restrain each policy improvement stage. It yields a sequence of safe policies that determine the range of safe operation, called the safe set.
  arXiv  Detail & Related papers  (2020-02-24T09:20:03Z)

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.