Know Your Body: A Harness for Direct and Self-Improving Robot Control with VLMs

arXiv:2609.28530 · cs.RO · Submitted 2026-09-22 · Read on arXiv

cs.RO

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 19 pages, 5 figures, 7 tables. Project page: https://loule0-0.github.io/KnowBody/

Project page: https://loule0-0.github.io/KnowBody

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Provenance — 6× in this paper · explained in 3 episodes
• Verifier — 5× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Affordance — 3× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Show-Harness: Just a VLM Agent Can Play Robots
• Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Agent as Policy for Robotic Manipulation
• PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning
• HumanCLAW: Can Vision-Language Models Act Through a Body?
• Reflective VLA: In-Context Action Consequences Make VLAs Generalize
• ASPIRE: Agentic /Skills Discovery for Robotics
• In-Context Learning Enables Robot Action Prediction in LLMs
• Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• ARC: A Reasoning Recipe for Robot Foundation Models
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies