arXiv 2610.26848 · cs.RO · 2026-10-19
Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
Zekai Li, Jiayi Yu, Jinlu Zhang, Luyang Niu, Zekun Qi, Jun Zheng, Xuchuan Chen, Dairu Liu and 3 more
Galbot
In latest · #378 Added on 2026-10-24
One-line takeaway
Galbot investigates where physical recursive self-improvement comes from, introducing PhysRSI-eval with five axes (first-attempt performance, efficiency, success, budget, improvement from failure), evaluating VLMs on 18 manipulation tasks and proposing a Physical Barrel Hypothesis.
银河通用(Galbot)研究物理世界递归自我改进的来源:提出 PhysRSI-eval,从首次表现、效率、成功、预算、从失败中提升五个维度评测,在 18 个操作任务上比较不同视觉语言模型,并提出“物理木桶假说”。
Read on arXiv PDF Hugging Face discussion Project page
Topics & disciplines
More on Robotics & embodied AI
- Has GPT-6-Astra Achieved Embodied Intelligence? ▲421
- In-Context Learning for Robots: Methods and Applications ▲388
- GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives ▲310
- HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents ▲294
- Reflex: Visually Grounded Reactive Humanoid Control ▲154
More on Benchmarks & data
- Spectral-Spatial Interpretation 2026-10-15 · ▲753
- StreamEval: Evaluating Interactive Streaming Video Generation Models 2026-10-20 · ▲649
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business 2026-10-14 · ▲388
- RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations 2026-10-01 · ▲287
- FDE-Gym: The Last Mile of AI Delivery 2026-10-15 · ▲270
More from Galbot
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.