DexNeRF first to utilize NeRF for grasping transparent objects limitation: needs time-consuming per-scene training, not real-time running 多物体连续性抓取存在困难,每次抓取新物体都需要对场景进行重建,retrain一个新NeRF EvoNeRF Instant-NGP & MIRA evolve the NeRF after each grasp costs 7s for each up...
为了支持高效的零样本部件级抓取,GraspSplats执行目标级查询、条件部件级查询和抓取采样。与基于NeRF的方法不同,后者需要从隐式MLP中提取与语言对齐的特征和几何形状,这需要昂贵的渲染过程,而GraspSplats则直接在高斯原语上操作,以实现高效的定位和抓取查询。开放词汇目标查询。我们首先执行目标级开放词汇查询(例如,“杯子...
为了支持高效的零样本部件级抓取,GraspSplats执行目标级查询、条件部件级查询和抓取采样。与基于NeRF的方法不同,后者需要从隐式MLP中提取与语言对齐的特征和几何形状,这需要昂贵的渲染过程,而GraspSplats则直接在高斯原语上操作,以实现高效的定位和抓取查询。开放词汇目标查询。我们首先执行目标级开放词汇查询(例如,“杯子...
We present dGrasp, an implicit grasp policy with an enhanced optimization landscape. This landscape is defined by a NeRF-informed grasp value function. The neural network representing this function is trained on simulated grasp demonstrations. During training, we use an auxiliary loss to guide not ...
GraspSplats中的每个组件都非常高效,并且在经验上比现有工作快一个数量级(10倍)——包括计算二维参考特征、优化三维表示和生成二指抓取建议。这使得在手臂扫描的同时并行生成GraspSplats表示成为可能。在实验中,GraspSplats的性能优于基于NeRF的方法(如F3RM和LERF-TOGO)以及其他基于点的方法。
However, I only got 16 honor as a Lt for a loss, which sounds like a nerf (I can't recall how much I used to get but surely it was more than that). Tol Barad was still paying full honor for a victory. Reply With Quote ...
dGrasp: NeRF-Informed Implicit Grasp Policies with Supervised Optimization Slopes During training, we use an auxiliary loss to guide not only the weight updates of this network but also the update how the slope of the optimization landscape changes. This loss is computed on the demonstrated grasp...
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects The ability to grasp and manipulate transparent objects is a major challenge for robots. Existing depth cameras have difficulty detecting, localizing, and inferring the geometry of such objects. We propose using neural radiance field...
自动驾驶公司重感知轻地图的大背景下,SLAM算法工程师是不是要失业? 智能驾驶技术哪家最强? ICRA2023个人回顾:涉及人形机器人、人机交互的思考 高精地图 为什么从“小甜甜”变成了“牛夫人”? 即将开讲!《视觉惯性SLAM》挑战赛第一部分第③期来啦! 基于NeRF SLAM的 GitHub上线!
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