ProTracer: Proprioception-Guided Failure Diagnosis in Robot Manipulation

arXiv:2609.21369 · cs.RO, cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Project page: https://protracer-failure.github.io

Terminology

• Calibration — 14× in this paper · explained in 7 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Adaptation — 8× in this paper · explained in 1 episode
• Grounding — 6× in this paper · explained in 3 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Training-Free Framework — 4× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• AUROC — 2× in this paper · explained in 2 episodes
• Aperture — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Credit assignment — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Failure Taxonomy — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
• RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction
• ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
• THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
• RePLan: Robotic Replanning with Perception and Language Models
• VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Gemini Robotics: Bringing AI into the Physical World
• RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

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