ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation

arXiv:2609.18242 · cs.RO · Submitted 2026-09-16 · Read on arXiv

cs.RO

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Distillation — 14× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Force-aware Vision-Language-Action — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes

Sources

• ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
• ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
• TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
• FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
• Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
• DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
• From Imitation to Refinement -- Residual RL for Precise Assembly
• Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections
• FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation
• CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning
• ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation
• FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation
• ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
• ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
• PhaForce: Phase-Scheduled Visual-Force Policy Learning with Slow Planning and Fast Correction for Contact-Rich Manipulation
• Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
• FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation
• Real-Time Execution of Action Chunking Flow Policies
• Residual Reinforcement Learning for Robot Control
• OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

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