PhysCaP: Grounding Code-as-Policy Agent with Physics-Informed Exploration
- 类型：arxiv
- 标识：2608.21031
- 链接：https://arxiv.org/abs/2608.21031
- 主分类：agent
- 形态：method
- TLDR：We present PhysCaP, a Physics-Informed Code-as-Policy agent for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstrations, they rely on passive observation and fail to infer latent physical properties critical for manipulation. PhysCaP augments code-as-policy frameworks with a physics-informed exploration layer that enables explicit information-seeking through interaction. It introduces training-free physical property extraction modules that estimate object mass and stiffness from robot proprioception without additional sensors. To balan
- 待LLM分类：否
- 标题中文：PhysCaP：用 physics-informed 探索对 Code-as-Policy Agent 进行 grounding
- TLDR中文：我们提出 PhysCaP，一种面向机器人操作主动感知的物理信息驱动 code-as-policy agent。尽管 vision-language-action 策略擅长模仿示教，但它们依赖被动观察，无法推断对操作至关重要的潜在物理属性。PhysCaP 在 code-as-policy 框架基础上引入物理信息驱动的探索层，通过交互实现显式信息获取。它提出了无需训练的物理属性提取模块，仅依靠机器人本体感知即可估算物体质量和刚度，无需额外传感器。为了在 balan
- 来源文件：
- /inbox/tom/_candidates/2026-08-25-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-08-25-agent-memory-tool-use-candidates.json