Robo-Harness K1: Harnessing Robot-Use Agents via Perception Augmentation

arXiv:2609.29389 · cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/robocurve/inspect-robots

Terminology

• Accuracy — 35× in this paper · explained in 1 episode
• Fine-Tuning — 16× in this paper · explained in 5 episodes
• Generalization — 13× in this paper · explained in 13 episodes
• Grounding — 11× in this paper · explained in 3 episodes
• Trace — 11× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Adaptation — 7× in this paper · explained in 1 episode
• Calibration — 5× in this paper · explained in 7 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Zero-Shot Transfer — 5× in this paper · explained in 4 episodes
• Sample Efficiency — 4× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Vision-Language-Action (VLA) Models — 3× in this paper · explained in 2 episodes
• Vision-language models — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Affordance — 2× in this paper · explained in 1 episode
• Agent Harness — 2× in this paper · explained in 2 episodes
• Cross-Embodiment Transfer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Visual Grounding — 2× in this paper · explained in 2 episodes
• Agentic Loop — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Qwen3-VL Technical Report
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• SAM 3: Segment Anything with Concepts
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• Show-Harness: Just a VLM Agent Can Play Robots
• Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting
• LoRA: Low-Rank Adaptation of Large Language Models
• VIA: Visual Interface Agent for Robot Control
• RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
• TAPNext++: What's Next for Tracking Any Point (TAP)?
• Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization
• Code as Policies: Language Model Programs for Embodied Control
• Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
• GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models

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