OTHER
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter I. Corke
- 发表年份
- 2017
- 访问权限
- 开放获取
摘要
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently. Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.
关键词
cs.ROcs.AIcs.CVcs.LGeess.SY
相关论文
OTHER
📊 1 引用
一种面向线弧增材制造的电动汽车结构可制造性拓扑优化的双环框架
Qiang Cui, Chuan Yu, Daoqian Yang 等 5 位作者
Robotics and Computer-Integrated Manufacturing · 2026
OTHER
📊 0 引用
几何数字孪生：一种用于航空发动机装配精度预测的数字智能模型
Ke Shang, Xin Jin, Teli Xu 等 7 位作者
Robotics and Computer-Integrated Manufacturing · 2026
OTHER
📊 0 引用
通过人工智能驱动的机器人技术革新产业
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
OTHER
📊 0 引用
新型大口径偏置馈电可展开天线设计与动态性能预测
Chuang Shi, Tianming Liu, Ning Xue 等 9 位作者
Aerospace Science and Technology · 2026