Diffusion-Informed Probabilistic Contact Search for Multi-Finger Manipulation
Planning contact-rich interactions for multi-finger manipulation is challenging due to the high-dimensionality and hybrid nature of dynamics. Recent advances in data-driven methods have shown promise, but are sensitive to the quality of training data. Combining learning with classical methods like t...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
01.10.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Planning contact-rich interactions for multi-finger manipulation is
challenging due to the high-dimensionality and hybrid nature of dynamics.
Recent advances in data-driven methods have shown promise, but are sensitive to
the quality of training data. Combining learning with classical methods like
trajectory optimization and search adds additional structure to the problem and
domain knowledge in the form of constraints, which can lead to outperforming
the data on which models are trained. We present Diffusion-Informed
Probabilistic Contact Search (DIPS), which uses an A* search to plan a sequence
of contact modes informed by a diffusion model. We train the diffusion model on
a dataset of demonstrations consisting of contact modes and trajectories
generated by a trajectory optimizer given those modes. In addition, we use a
particle filter-inspired method to reason about variability in diffusion
sampling arising from model error, estimating likelihoods of trajectories using
a learned discriminator. We show that our method outperforms ablations that do
not reason about variability and can plan contact sequences that outperform
those found in training data across multiple tasks. We evaluate on simulated
tabletop card sliding and screwdriver turning tasks, as well as the screwdriver
task in hardware to show that our combined learning and planning approach
transfers to the real world. |
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DOI: | 10.48550/arxiv.2410.00841 |