ForceBench: Benchmarking Force Integration for Robotic Manipulation
cs.RO
Submitted: 2026-10-14
Updated: 2026-10-14
Comments: 14 pages, 8 figures. Project page: https://forcebench-platform.github.io/
Project page: https://forcebench-platform.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
- FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
- Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
- ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
- FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
- PhaForce: Phase-Scheduled Visual-Force Policy Learning with Slow Planning and Fast Correction for Contact-Rich Manipulation
- FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
- Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
- FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation
- Towards Generalizable Robotic Manipulation in Dynamic Environments
- AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
- Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
- TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
- ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching
- Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models
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- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies