RoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents

arXiv:2609.25636 · cs.RO · Submitted 2026-09-22 · Read on arXiv

cs.RO

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/AutoLab-SAI-SJTU/RoboFollow

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Grounding — 23× in this paper · explained in 3 episodes
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Generalization — 11× in this paper · explained in 13 episodes
• World Action Models — 6× in this paper
• Visual Grounding — 5× in this paper · explained in 2 episodes
• Classifier-Free Guidance — 4× in this paper
• Foundation Model — 4× in this paper · explained in 3 episodes
• Completion Rate — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Action Primitives — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Recent Vision-Language-Action — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
• Motus: A Unified Latent Action World Model
• Multi-agent Embodied AI: Advances and Future Directions
• Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy
• LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models
• RT-1: Robotics Transformer for Real-World Control at Scale
• Octo: An Open-Source Generalist Robot Policy
• X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
• InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
• SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
• TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
• SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
• QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization

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