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DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics

Zhiao Huang · Feng Chen · Yewen Pu · Chunru Lin · Hao Su · Chuang Gan

Great Hall & Hall B1+B2 (level 1) #430
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Thu 14 Dec 8:45 a.m. PST — 10:45 a.m. PST


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.

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