Multifingered force-aware control for humanoid robots

• URL: http://arxiv.org/abs/2603.08142v1
• Date: Mon, 09 Mar 2026 09:17:45 GMT
• Title: Multifingered force-aware control for humanoid robots
• Abstract summary: We address force-aware control and force distribution in robotic platforms with multi-fingered hands.<n>We design a controller that adapts the motion of the torso, arm, wrist, and fingers, redistributing forces to maintain stable contact with objects of varying mass distribution or unstable contacts.<n>We validate our framework on a balancing task with five objects, achieving a $82.7%$ success rate, and further evaluate it in multi-object scenarios, achieving $80%$ accuracy.
• Score: 5.099311163246841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address force-aware control and force distribution in robotic platforms with multi-fingered hands. Given a target goal and force estimates from tactile sensors, we design a controller that adapts the motion of the torso, arm, wrist, and fingers, redistributing forces to maintain stable contact with objects of varying mass distribution or unstable contacts. To estimate forces, we collect a dataset of tactile signals and ground-truth force measurements using five Xela magnetic sensors interacting with indenters, and train force estimators. We then introduce a model-based control scheme that minimizes the distance between the Center of Pressure (CoP) and the centroid of the fingertips contact polygon. Since our method relies on estimated forces rather than raw tactile signals, it has the potential to be applied to any sensor capable of force estimation. We validate our framework on a balancing task with five objects, achieving a $82.7\%$ success rate, and further evaluate it in multi-object scenarios, achieving $80\%$ accuracy. Code and data can be found here https://github.com/hsp-iit/multifingered-force-aware-control.

Related papers

• ForceBand: Learning Forceful Manipulation with sEMG [55.186900419710746]
  We introduce ForceBand, a low-cost wrist-worn sEMG system that turns human muscle activity into force-enriched demonstrations.<n>We pre-train an EMG2Force model that predicts per-finger forces from sEMG and IMU signals.<n>Experiments show that ForceBand recovers fine-grained fingertip interactions with over 50% lower force prediction error than vision-based baselines.
  arXiv  Detail & Related papers  (2026-06-24T17:59:35Z)
• FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning [54.93624123794711]
  Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost.<n>We present Neural External Torque Estimation (torque), a data-driven method that estimates external joint torques without needing any dedicated force sensors.<n>Next enables force-aware teleoperation on low-cost arms and improves policy learning through Force-Informed Re-Sampling Training.
  arXiv  Detail & Related papers  (2026-06-10T17:59:35Z)
• OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation [60.609604885961716]
  We present textbfOmniViTac, a large-scale visuo-tactile-action dataset comprising $21,000+$ trajectories across $86$ tasks and $100+$ objects.<n>We propose textbf OmniVTA, a world-model-based visuo-tactile manipulation framework that integrates four tightly coupled modules.
  arXiv  Detail & Related papers  (2026-03-19T17:52:42Z)
• FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid [41.604822515681256]
  Maintaining balance under external hand forces is critical for humanoid bimanual manipulation.<n>We propose textbfFAME, a force-adaptive reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-03-09T21:49:00Z)
• 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)
• Learning Force-Regulated Manipulation with a Low-Cost Tactile-Force-Controlled Gripper [18.40849472506029]
  TF-Gripper is a low-cost force-controlled parallel-jaw gripper that integrates tactile sensing as feedback.<n> RETAF is a framework that decouples grasping force control from arm pose prediction.<n>We evaluate TF-Gripper and RETAF across five real-world tasks requiring precise force regulation.
  arXiv  Detail & Related papers  (2026-02-10T17:36:33Z)
• 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)
• Feel the Force: Contact-Driven Learning from Humans [52.36160086934298]
  Controlling fine-grained forces during manipulation remains a core challenge in robotics.<n>We present FeelTheForce, a robot learning system that models human tactile behavior to learn force-sensitive manipulation.<n>Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks.
  arXiv  Detail & Related papers  (2025-06-02T17:57:52Z)
• FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors [18.88211706267447]
  We tackle the problem of estimating 3D contact forces using vision-based tactile sensors. Our goal is to estimate contact forces over a large range (up to 15 N) on any objects while generalizing across different vision-based tactile sensors.
  arXiv  Detail & Related papers  (2024-10-02T21:28:19Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• Fine Robotic Manipulation without Force/Torque Sensor [9.908082209033612]
  A typical 6-axis Force/Torque (F/T) sensor is mounted between the robot's wrist and the end-effector in order to measure the forces exerted by the environment onto the robot (the external wrench) Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot's internal signals are limited in scope. We present a Neural Network based method and argue that by devoting particular attention to the training data structure, it is possible to accurately estimate the
  arXiv  Detail & Related papers  (2023-01-31T05:06:04Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)

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.