ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation
cs.RO
Submitted: 2026-10-11
Updated: 2026-10-11
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
- Show-Harness: Just a VLM Agent Can Play Robots
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- Transferring the Intelligence of VLMs to Robotic Control
- RoboICL: Embodied In-Context Learning with GPT-6 Astra
- ASPIRE: Agentic /Skills Discovery for Robotics
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- HarnessPAI: An Evolving Harness for Physical AI
- In-Context Learning Enables Robot Action Prediction in LLMs
- Playful Agentic Robot Learning
- Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
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