Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.06641

Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation

Moein Forouhar·Kosar Behnia·Anirvan Dutta·Hamid Sadeghian·Ville Kyrki·Sami Haddadin·Gokhan Alcan·Eckehard Steinbach
View original source ↗Read source document ↗Export metadata ↓

Abstract

Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the grasp points and the unactuated interior. We present a hierarchical planning framework that combines a learned cloth model with a reduced-order whole-body model of a dual-arm mobile manipulator. A physics-residual conditional recurrent variational autoencoder (p-cRVAE) predicts the full cloth configuration from grasp-point observations by learning a residual correction to a computationally efficient linearized physics model, limiting error accumulation over 40-step planning horizon. The predicted cloth dynamics are embedded in a model predictive path integral (MPPI) planner using a reduced-order representation of a dual-arm mobile manipulator that preserves the non-holonomic base constraint and arm workspace limits. An MPC layer subsequently refines the sampled motion into smooth, executable references for whole-body control. The reduced-order formulation achieves tracking performance comparable to the full 17-DoF model while reducing computation time by approximately 80%. Across four co-transportation scenarios and two carrying speeds, the proposed framework maintains cloth-obstacle clearance where a corner-following baseline results in collisions, while whole-body refinement reduces final cloth deformation from 0.93\,m to 0.28\,m.

Explore related subjects

cs.RO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moein Forouhar, Kosar Behnia, Anirvan Dutta, Hamid Sadeghian, Ville Kyrki, Sami Haddadin, Gokhan Alcan, Eckehard Steinbach. 2026-10-05. Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation. https://arxiv.org/abs/2610.06641

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning

Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach.

cs.RO↗
arXiv2026

Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling

Video planning has emerged as a flexible framework for robot manipulation, in which a generative model predicts a video of task completion, and a downstream module translates the predicted frames into actions. However, existing methods typically ignore information from past interactions, limiting their ability to adapt to latent physical properties that can only be revealed through trial and error, such as whether a door should be pushed or pulled, or how friction affects object dynamics. When a plan fails, these methods usually replan from scratch without leveraging the information revealed by the failure. To address this limitation, we introduce RELIC, REplanning with Latent embedding refInement and Candidate rejection, a video planning framework that adapts to hidden physical properties from test-time failures. RELIC optimizes a latent embedding that captures the environment's hidden physical properties from interaction videos and introduces a rejection-based sampling mechanism that filters out hypotheses inconsistent with prior failures. Across eight tasks in two simulation suites, RELIC consistently reduces the number of replanning steps required for success, and linear probes show that its embedding captures the hidden parameters from the interaction itself rather than from scene appearance. Across four challenging real-world robotic manipulation tasks involving hidden interaction modes, e.g., friction, center of mass, and object mass, RELIC raises the one-shot replanning success rate of a video planning baseline from 30.0% to 63.8% after a single physical interaction.

cs.RO↗
arXiv2026

Least Restrictive Hyperplane Control Barrier Functions

Control Barrier Functions (CBFs) can provide provable safety guarantees for dynamic systems. However, finding a valid CBF for a system of interest is often non-trivial, especially for systems with low computational resources, higher-order dynamics, and moving close to obstacles of complex shape. A common solution to this problem is to use a purely distance-based CBF. In this paper, we study Hyperplane CBFs (H-CBFs), where a hyperplane separates the agent from the obstacle. First, we note that the common distance-based CBF is a special case of an H-CBF where the hyperplane is a supporting hyperplane of the obstacle that is orthogonal to a line between the agent and the closest point of the obstacle. We then show that a less conservative CBF can be found by optimising over the orientation of the supporting hyperplane, in order to find the Least Restrictive Hyperplane CBF. This enables us to maintain the safety guarantees while allowing controls that are closer to the desired ones, especially when moving fast and passing close to obstacles. We illustrate the approach on a double integrator dynamical system with acceleration constraints, moving through a group of arbitrarily shaped static and moving obstacles, and show that the proposed approach reduces the average gap between desired and safe controls by an order of magnitude.

cs.RO↗