DiffVL: 視覚言語駆動の微分可能物理による軟体操作のスケーリング
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
非専門家が視覚と言語で指定した軟体操作タスクを、大規模言語モデルで最適化目的に変換し、微分可能物理シミュレータで解く手法を提案。
著者: Zhiao Huang, Feng Chen, Yewen Pu, Chunru Lin, Hao Su, Chuang Gan
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems. Using a well-crafted optimization objective, the solver can quickly converge onto a valid trajectory. However, writing the appropriate objective functions requires expert knowledge, making it difficult to collect a large set of naturalistic problems from non-expert users. We introduce DiffVL, a method that enables non-expert users to communicate soft-body manipulation tasks -- a combination of vision and natural language, given in multiple stages -- that can be readily leveraged by a differential physics solver. We have developed GUI tools that enable non-expert users to specify 100 tasks inspired by real-life soft-body manipulations from online videos, which we'll make public. We leverage large language models to translate task descriptions into machine-interpretable optimization objectives. The optimization objectives can help differentiable physics solvers to solve these long-horizon multistage tasks that are challenging for previous baselines.
