Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation

• URL: http://arxiv.org/abs/2609.13779v1
• Date: Sat, 12 Sep 2026 07:46:53 GMT
• Title: Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation
• Abstract summary: Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion.<n>This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation.
• Score: 14.501012360823886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces. This is challenging under floating-base dynamics and changing support contacts, particularly when end-effector force/torque sensing is unavailable for control. This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation. The proposed method provides a structured estimate of the end-effector force as an explicit policy observation, enabling force-guided contact behavior without using an end-effector force/torque sensor for control. The force estimate is obtained by combining generalized momentum observation, contact-constrained wrench projection, and temporal residual learning: the model-based components extract the physically structured part of the whole-body disturbance, while the residual network compensates the remaining motion-dependent bias. The estimated force is integrated into a mode-conditioned whole-body policy with an axis-wise force/position selector, allowing free-space motion, pure force regulation, and hybrid force/position control within one controller. Simulation results demonstrate improved sensorless force estimation and force-control performance. Hardware experiments further validate the proposed controller through quantitative valve-rotation and hybrid wiping evaluations, together with force-guided door opening and zero-force human-guided motion on a real wheeled-legged platform.

Related papers

• TAC-LOCO: Unified Whole-Body Control for Quadrupedal TACtile-Informed LOCO-Manipulation [13.785492911693098]
  Dynamic loco-manipulation requires legged robots to coordinate whole-body motion while maintaining stable physical interaction with grasped objects under uncertain external forces.<n>We propose TAC-LOCO, a tactile-augmented unified reinforcement learning framework that encodes tactile array observations from compliant grippers into a compact latent representation.<n>We deploy the policy zero-shot on a Unitree Go2 with an Interbotix WidowX 250 arm and tactile gripper, demonstrating dynamic tactile-informed loco-manipulation under varying external interactions.
  arXiv  Detail & Related papers  (2026-07-11T05:45:24Z)
• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation [17.63466252699545]
  Human-like dexterous hands offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware.<n>We present a sim-to-real reinforcement learning method that leverages dense tactile feedback combined with joint torque sensing.<n>This is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.
  arXiv  Detail & Related papers  (2026-07-06T11:15:36Z)
• Object Manipulation of the Variable Topology Truss system [7.177683001482471]
  This paper presents an object manipulation strategy for the Variable Topology Truss system.<n>The truss robot comprises actuated truss members connected by passive spherical joints.<n>We introduce a hybrid control framework that regulates position and force concurrently without explicit decoupling.
  arXiv  Detail & Related papers  (2026-05-13T06:55:11Z)
• ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching [53.30290192030814]
  ForceFlow is a force-aware reactive framework built upon flow matching.<n>We show that ForceFlow achieves a 37% success rate improvement over the strong baseline ForceVLA.<n>It also exhibits accurate force signal prediction and superior performance in contact force self-regulation.
  arXiv  Detail & Related papers  (2026-05-11T13:27:00Z)
• ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation [75.39847750457626]
  ForceVLA2, an end-to-end vision-language-action framework, equips robots with hybrid force-position control and explicit force awareness.<n>To support learning and evaluation, we construct ForceVLA2-Dataset, containing 1,000 trajectories over 5 contact-rich tasks.<n>Experiments show that ForceVLA2 substantially improves success rates and reliability in contact-rich manipulation.
  arXiv  Detail & Related papers  (2026-03-16T12:03:58Z)
• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
  We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
  arXiv  Detail & Related papers  (2026-03-03T18:59:29Z)
• Safe Whole-Body Loco-Manipulation via Combined Model and Learning-based Control [46.47619556874003]
  We propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning policy for legged locomotion.<n>The admittance controller maps external wrenches--such as those applied by a human during physical interaction--into desired end-effector velocities, allowing for compliant behavior.<n>We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor.
  arXiv  Detail & Related papers  (2026-03-02T22:31:12Z)
• Synchronized Online Friction Estimation and Adaptive Grasp Control for Robust Gentle Grasp [3.201799966622367]
  We propose a new particle filter-based method for real-time estimation of the friction coefficient using vision-based tactile sensors.<n>This estimate is seamlessly integrated into a reactive controller that dynamically modulates grasp force to maintain a stable grip.
  arXiv  Detail & Related papers  (2026-02-02T12:21:27Z)
• UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
  We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
  arXiv  Detail & Related papers  (2026-02-01T11:03:01Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)

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.