Adaptive Manipulation Potential and Haptic Estimation for Tool-Mediated Interaction

• URL: http://arxiv.org/abs/2603.10352v1
• Date: Wed, 11 Mar 2026 02:57:55 GMT
• Title: Adaptive Manipulation Potential and Haptic Estimation for Tool-Mediated Interaction
• Abstract summary: This paper introduces a parameterized Equilibrium Manifold (EM) as a unified representation for tool-mediated interaction.<n>We develop a closed-loop framework that integrates haptic estimation, online planning, and adaptive stiffness control.<n>The framework is validated through simulation and over 260 real-world screw-loosening trials.
• Score: 14.888648782445694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving human-level dexterity in contact-rich, tool-mediated manipulation remains a significant challenge due to visual occlusion and the underdetermined nature of haptic sensing. This paper introduces a parameterized Equilibrium Manifold (EM) as a unified representation for tool-mediated interaction, and develops a closed-loop framework that integrates haptic estimation, online planning, and adaptive stiffness control. We establish a physical-geometric duality using an adaptive manipulation potential incorporating a differentiable contact model, which induces the manifold's geometric structure and ensures that complex physical interactions are encapsulated as continuous operations on the EM. Within this framework, we reformulate haptic estimation as a manifold parameter estimation problem. Specifically, a hybrid inference strategy (haptic SLAM) is employed in which discrete object shapes are classified via particle filtering, while the continuous object pose is estimated using analytical gradients for efficient optimization. By continuously updating the parameters of the manipulation potential, the framework dynamically reshapes the induced EM to guide online trajectory replanning and implement uncertainty-aware impedance control, thereby closing the perception-action loop. The system is validated through simulation and over 260 real-world screw-loosening trials. Experimental results demonstrate robust identification and manipulation success in standard scenarios while maintaining accurate tracking. Furthermore, ablation studies confirm that haptic SLAM and uncertainty-aware stiffness modulation outperform fixed impedance baselines, effectively preventing jamming during tight tolerance interactions.

Related papers

• VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations [8.606058461805732]
  This letter introduces Variable Impedance Diffusion Policy (PVID), an imitation learning-based variable impedance control framework.<n>By mapping to stiffness profiles, VIDP jointly predicts pose actions and task compliance without force sensors.<n>Real-world experiments show that VIDP significantly outperforms fixed-impedance baselines in task success rate.
  arXiv  Detail & Related papers  (2026-08-06T16:03:00Z)
• Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation [10.23547821843138]
  Deformable tool dynamics introduce nonlinear viscoelastic that decouples wrist-mounted force measurements from true contact forces.<n>This paper presents a data-driven framework for contact force estimation in Deformable Tool Manipulation.
  arXiv  Detail & Related papers  (2026-07-08T16:01:23Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• Interaction-Enabled Hartree Fixed Points in Fermionic Resetting Dynamics [0.0]
  In resetting dynamics, a system is repeatedly coupled to and decoupled from ancillary degrees of freedom that are recoupled between interactions.<n>This provides a versatile route to engineer nonequilibrium states and constitutes a powerful and analytically transparent framework for studying nonequilibrium dynamics in fermionic models.<n>We introduce a Hartree mean-field treatment of density-density interactions that preserves closure of the SPDM dynamics while producing genuinely nonlinear behavior.
  arXiv  Detail & Related papers  (2026-03-16T17:14:08Z)
• System Identification under Constraints and Disturbance: A Bayesian Estimation Approach [5.58001571731715]
  We introduce a Bayesian system identification (SysID) framework for jointly estimating robot's state trajectories and physical parameters with high accuracy.<n>It embeds physically consistent inverse dynamics, contact and loop-closure constraints, and fully featured joint friction models as hard, stage-wise equality constraints.
  arXiv  Detail & Related papers  (2026-02-18T10:45:30Z)
• Equivariant Evidential Deep Learning for Interatomic Potentials [55.6997213490859]
  Uncertainty quantification is critical for assessing the reliability of machine learning interatomic potentials in molecular dynamics simulations.<n>Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance.<n>We propose textitEquivariant Evidential Deep Learning for Interatomic Potentials ($texte2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly.
  arXiv  Detail & Related papers  (2026-02-11T02:00:25Z)
• Optimal Effective Hamiltonian for Quantum Computing and Simulation [1.0359978670015826]
  We establish the Least Action Unitary Transformation as the fundamental principle for effective models.<n>We validate this framework against experimental data from superconducting quantum processors.<n>This work provides a systematic, experimentally validated route for high-precision system learning and Hamiltonian engineering.
  arXiv  Detail & Related papers  (2026-02-03T15:09:29Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning [52.23804865017831]
  We address the problem of verifying closed-loop contraction in nonlinear control systems whose controller and contraction metric are both parameterized by neural networks.<n>We derive a tractable and scalable sufficient condition for closed-loop contractivity that reduces to checking that the dominant eigenvalue of a symmetric Metzler matrix is nonpositive.
  arXiv  Detail & Related papers  (2025-12-01T23:06:56Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• The power of dynamic causality in observer-based design for soft sensor applications [0.7965327033045845]
  This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis.<n>Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations that fail to capture the temporal evolution of complex systems.
  arXiv  Detail & Related papers  (2025-09-14T16:27:58Z)
• Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers [0.0]
  This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering.<n>A Convolutional Neural Network (CNN), enhanced with image preprocessing, detects structural markers from chaser imagery.<n>A UKF framework is selected for its ability to handle nonlinear relative dynamics to estimate the full relative pose.
  arXiv  Detail & Related papers  (2025-07-22T04:13:03Z)
• Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions [46.75046795995564]
  In AIMC hardware, the trainable weights are represented by the conductance of resistive elements and updated using consecutive electrical pulses.<n>While the conductance changes by a constant in response to each pulse, in reality, the change is scaled by asymmetric and non-linear response functions, leading to a non-ideal training dynamic.<n>This paper provides a theoretical foundation for gradient-based training on AIMC hardware with non-ideal response functions.
  arXiv  Detail & Related papers  (2025-02-10T09:56:15Z)

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.