Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection

arXiv:2607.14236 · cs.RO, cs.AI, cs.LG · Submitted 2026-07-15 · Read on arXiv

cs.RO, cs.AI, cs.LG

Submitted: 2026-07-15

Updated: 2026-09-23

Code: https://github.com/flexivrobotics/flexiv_tdk

Terminology

• Generalization — 10× in this paper · explained in 13 episodes
• Alignment — 8× in this paper · explained in 1 episode
• VLA Models — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Covariate Shift — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Angular Error — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• KV cache — 1× in this paper · explained in 3 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Octo: An Open-Source Generalist Robot Policy
• 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
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
• ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
• VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs
• FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
• SOP: A Scalable Online Post-Training System for Vision-Language-Action Models
• RoboPocket: Improve Robot Policies Instantly with Your Phone
• FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning
• TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
• Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
• ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
• Multi-Task Interactive Robot Fleet Learning with Visual World Models

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