发表机构
Rice University; Zhejiang University（莱斯大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出紧凑视觉触觉世界模型，结合轨迹级校准和想象学习，在举升任务中显著提升力预测精度和成功率，并区分了感知奖励与力约束控制的改进。
AI 中文摘要
准确的接触预测只有在支持有效决策时才对机器人操作有用。我们通过一个紧凑的、随机初始化的视觉触觉世界模型、轨迹级不确定性校准以及想象中行为初始化的演员-评论家学习来研究这一联系。在160次MuJoCo举升任务片段中，跨三个训练种子，添加触觉将端点力预测误差从1.058 N降至0.228 N，区间峰值误差从2.724 N降至0.523 N。然而，触觉持久性分别实现了更低的误差0.095 N和0.498 N。两轮探索性控制实验包括在40个独立测试初始条件下的680次执行。在全新测试环境上进行匹配的奖励修订，将分布内10厘米举升成功率从20.0%提升至93.3%，而每指8 N预算内的成功率仅达到33.3%，相比之下，力反馈的成功率为70.0%。校准裕度以减少力违规为代价降低了任务完成率。在一项对公开GelSight记录的独立研究中，力回归器实现了0.04234 N的误差，但帧级校准仅覆盖了完整轨迹的15.80%；轨迹级校准在标称90%覆盖率下将这一比例提升至87.36%。综合这些发现，区分了感知和任务奖励的改进与力约束控制的改进。证据仅限于公开的传感记录和模拟器执行，未展示两者之间的迁移。
英文摘要
Accurate tactile forecasts need not improve force-constrained control. We study a 652,157-parameter action-conditioned visuotactile world model with matched behavior cloning, policy learning in imagination, independent reactive implicit Q-learning, and model-assisted force feedback. A fixed protocol executes 34 policies on 120 fresh MuJoCo environments spanning geometry and physical-parameter shifts, plus 324 independently replayed action branches on 12 additional ID environments. Visuotactile dynamics reduce force action-effect MAE from 0.413 N for persistence to 0.338 N. Model-assisted feedback raises ID force-budgeted success from 73.3% to 93.3%, with paired difference +20.0 [+6.7,+33.4] percentage points (95% CI), with the difference occurring during scripted lowering. Its pooled difference is +3.9 [-4.5,+11.7] points. Imagined RL achieves 11.9% pooled joint success versus 25.0% for reactive IQL. An empirical tactile-residual stress test adds 330 executions. The evidence concerns rigid-box lifting after a common approach, without physical-robot transfer or a closed-loop safety guarantee.
Comments8 pages, 2 figures. Code and tabulated results included as ancillary material
