Characterization of Constraints in Flexible Unknown Environments
- URL: http://arxiv.org/abs/2603.24813v1
- Date: Wed, 25 Mar 2026 20:48:25 GMT
- Title: Characterization of Constraints in Flexible Unknown Environments
- Abstract summary: This paper presents an online path planning algorithm for safe autonomous manipulation of a flexibly constrained object in an unknown environment.<n>Used in tandem, these methods allow a robot to simultaneously explore, characterize, and manipulate an elastic system safely.
- Score: 5.4200377321709565
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents an online path planning algorithm for safe autonomous manipulation of a flexibly constrained object in an unknown environment. Methods for real time identification and characterization of perceived flexible constraints and global stiffness are presented. Used in tandem, these methods allow a robot to simultaneously explore, characterize, and manipulate an elastic system safely. Navigation without a-priori knowledge of the system is achieved using constraint exploration based on local force and position information. The perceived constraint stiffness is considered at multiple poses along an object's (system) trajectory. Using stiffness eigenvector information, global stiffness behavior is characterized and identified using an atlas of simple mechanical constraints, such as hinges and planar constraints. Validation of these algorithms is carried out by simulation and experimentally. The ability to recognize several common simple mechanical constraints (such as a flexible hinge) in real time, and to subsequently identify relevant screw parameters is demonstrated. These results suggest the feasibility of simultaneous global constrain/stiffness exploration and safe manipulation of flexibly constrained objects. We believe that this approach will eventually enable safe cooperative manipulation in applications such as organ retraction and manipulation during surgery
Related papers
- Active Timepoint Selection for Learning Measure-Valued Trajectories [55.49809248697036]
Inferring continuous probability paths from snapshots is a fundamental challenge in domains like single-cell biology.<n>We introduce a framework which extends active experimentation to the space of sparse measures.<n>This yields an acquisition policy that iteratively selects measurement times to minimize uncertainty.
arXiv Detail & Related papers (2026-05-28T22:22:35Z) - Provably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties [26.41382315967256]
This article proposes a novel risk-bounded motion planning framework to address the unmet need for safe optimal motion planning.<n>Our approach integrates a rigid manipulator deep Koopman operator (RM-DeKO) model to predict the robot's state distribution under motion uncertainty.<n>We then introduce an efficient hierarchical verification method that combines parallel physics simulations with sum-of-squares (SOS) programming as a filter for fine-grained, formal certification of collision risk.
arXiv Detail & Related papers (2026-03-10T01:48:48Z) - Latent Space Reinforcement Learning for Multi-Robot Exploration [0.0]
We develop a novel system for autonomous mapping of unknown environments.<n>We compress high-fidelity occupancy maps into latent state vectors while preserving essential spatial information.<n>We introduce a weighted consensus mechanism that modulates reliance on shared data.
arXiv Detail & Related papers (2026-01-03T10:06:40Z) - Verification of Visual Controllers via Compositional Geometric Transformations [49.81690518952909]
We introduce a novel verification framework for perception-based controllers that can generate outer-approximations of reachable sets.<n>We provide theoretical guarantees on the soundness of our method and demonstrate its effectiveness across benchmark control environments.
arXiv Detail & Related papers (2025-07-06T20:22:58Z) - An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors [7.059232979003729]
This paper introduces the concept of generalized stiffness, extending the definition of stiffness to nonlinear time-varying grasp system models.<n>The proposed method achieves high precision and short probing time, while showing better adaptability to non-ideal objects.
arXiv Detail & Related papers (2024-12-03T09:55:00Z) - Learning Controlled Stochastic Differential Equations [61.82896036131116]
This work proposes a novel method for estimating both drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled differential equations with non-uniform diffusion.
We provide strong theoretical guarantees, including finite-sample bounds for (L2), (Linfty), and risk metrics, with learning rates adaptive to coefficients' regularity.
Our method is available as an open-source Python library.
arXiv Detail & Related papers (2024-11-04T11:09:58Z) - Resilient Constrained Learning [94.27081585149836]
This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task.
We call this approach resilient constrained learning after the term used to describe ecological systems that adapt to disruptions by modifying their operation.
arXiv Detail & Related papers (2023-06-04T18:14:18Z) - 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) - Automatic Exploration Process Adjustment for Safe Reinforcement Learning
with Joint Chance Constraint Satisfaction [2.127049691404299]
We propose an automatic exploration process adjustment method for safe reinforcement learning algorithms.
Our proposed method automatically selects whether the exploratory input is used or not at each time depending on the state and its predicted value.
Our method theoretically guarantees the satisfaction of the constraints with the pre-specified probability, that is, the satisfaction of a joint chance constraint at every time.
arXiv Detail & Related papers (2021-03-05T13:30:53Z) - Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable
Dynamical Systems [74.80320120264459]
We present an approach to learn such motions from a limited number of human demonstrations.
The complex motions are encoded as rollouts of a stable dynamical system.
The efficacy of this approach is demonstrated through validation on an established benchmark as well demonstrations collected on a real-world robotic system.
arXiv Detail & Related papers (2020-05-27T03:51:57Z) - Learning Control Barrier Functions from Expert Demonstrations [69.23675822701357]
We propose a learning based approach to safe controller synthesis based on control barrier functions (CBFs)
We analyze an optimization-based approach to learning a CBF that enjoys provable safety guarantees under suitable Lipschitz assumptions on the underlying dynamical system.
To the best of our knowledge, these are the first results that learn provably safe control barrier functions from data.
arXiv Detail & Related papers (2020-04-07T12:29:06Z) - Gaining a Sense of Touch. Physical Parameters Estimation using a Soft
Gripper and Neural Networks [3.0892724364965005]
There is not enough research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers.
We propose a trainable system for the regression of a stiffness coefficient and provided extensive experiments using the physics simulator environment.
Our system can reliably estimate the stiffness of an object using the Yale OpenHand soft gripper based on readings from Inertial Measurement Units (IMUs) attached to its fingers.
arXiv Detail & Related papers (2020-03-02T11:56:14Z)
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.