Diff-LfD: Contact-aware Model-based Learning from Visual Demonstration for Robotic Manipulation via Differentiable Physics-based Simulation and Rendering

Xinghao Zhu, Jinghan Ke, Zhixuan Xu, Zhixin Sun, Bizhe Bai, Jun Lv, Qingtao Liu, Yuwei Zeng, Qi Ye, Cewu Lu, Masayoshi Tomizuka, Lin Shao

Keywords: Learning from Demonstration, model-based robot learning, differentiable physics-based simulation and rendering

Abstract: Learning from Demonstration (LfD) is an efficient technique for robots to acquire new skills through expert observation, significantly mitigating the need for laborious manual reward function design. This paper introduces a novel framework for model-based LfD in the context of robotic manipulation. Our proposed pipeline is underpinned by two primary components: self-supervised pose and shape estimation and contact sequence generation. The former utilizes differentiable rendering to estimate object poses and shapes from demonstration videos, while the latter iteratively optimizes contact points and forces using differentiable simulation, consequently effectuating object transformations. Empirical evidence demonstrates the efficacy of our LfD pipeline in acquiring manipulation actions from human demonstrations. Complementary to this, ablation studies focusing on object tracking and contact sequence inference underscore the robustness and efficiency of our approach in generating long-horizon manipulation actions, even amidst environmental noise. Validation of our results extends to real-world deployment of the proposed pipeline. Supplementary materials and videos are available on our webpage.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Video: https://sites.google.com/view/diff-lfd

Website: https://sites.google.com/view/diff-lfd