# FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation

arXiv: https://arxiv.org/abs/2609.03889 · HTML: https://mlsanity.com/paper/2609.03889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 21 of 21)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing](https://mlsanity.com/paper/2604.05014/) (2026)
- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [QUAR-VLA: Vision-Language-Action Model for Quadruped Robots](https://mlsanity.com/paper/2312.14457/) (2023)
- [TLA: Tactile-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.08548/) (2025)
- [Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control](https://mlsanity.com/paper/2410.09309/) (2024)
- [MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation](https://mlsanity.com/paper/2503.13446/) (2025)
- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation](https://mlsanity.com/paper/2603.15169/) (2026)
- [FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.02142/) (2026)
- [Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?](https://mlsanity.com/paper/2412.09743/) (2024)
- [Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects](https://mlsanity.com/paper/2403.01694/) (2024)
- [ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks](https://mlsanity.com/paper/2508.08240/) (2025)
- [SG-VLA: Learning Spatially-Grounded Vision-Language-Action Models for Mobile Manipulation](https://mlsanity.com/paper/2603.22760/) (2026)
- [OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation](https://mlsanity.com/paper/2606.22174/) (2026)
- [TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.09337/) (2026)
- [Contact Localization for Robot Arms in Motion without Torque Sensing](https://mlsanity.com/paper/2011.03142/) (2020)
- [Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills](https://mlsanity.com/paper/2507.04229/) (2025)
- [Loss Function Considering Dead Zone for Neural Networks](https://mlsanity.com/paper/2402.00393/) (2024)
- [Cycloidal Quasi-Direct Drive Actuator Designs with Learning-based Torque Estimation for Legged Robotics](https://mlsanity.com/paper/2410.16591/) (2024)
- [Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators](https://mlsanity.com/paper/2507.06174/) (2025)

## Built on by (most cited first, 1 of 1)

- [Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation](https://mlsanity.com/paper/2609.23968/) (2026)

## Abstract (arXiv)

Contact-rich loco-manipulation requires a bridge between semantic action generation and physical interaction control. Existing Vision-language-action (VLA) models generate task-level actions from visual and linguistic observations, but cannot interpret the physical interactions induced by those actions. While the whole-body control (WBC) policy can stabilize the robot, it cannot distinguish task-relevant interaction forces from forces induced by external disturbances during manipulation. Although force/torque sensors provide direct measurements of physical interactions, retrofitting them entails additional hardware costs and substantial integration effort, particularly for platforms not designed with sensor integration in mind. To address this problem, we propose FWBC-VLA, a force-aware framework that bridges task-level VLA action generation and low-level whole-body compensation control for wheeled-legged robots. First, we introduce HSR-Force, a sensorless residual-torque estimator for inferring contact strength and its temporal variation. These contact estimates are then encoded as tokens and injected into the VLA action expert during action decoding, enabling the policy to perceive contact onset, sustained loading, and release. For loco-manipulation tasks, all parameters of the pretrained VLA backbone are fine-tuned on our WL&Arm Dataset, which comprises more than 5,000 episodes. Moreover, the robot's proprioceptive state, the Jacobian-derived body-frame force estimate, and the estimated contact state are jointly fed into a compensation generator to produce corrective actions. The manipulation-centric actions are subsequently combined with the corrective actions and passed to the WBC policy for execution. Real-world experiments on whiteboard wiping and door opening with a door closer demonstrate the effectiveness of our FWBC-VLA in contact-rich loco-manipulation.