2023-12-02-huang23c.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery	Poster	zvl2LuLTtgr	Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynamics}, yielding inaccuracies that manifest as the dynamical simulation-to-reality (sim-to-real) gap. Existing literature has proposed to close this gap by actively modifying specific simulator parameters to align the simulated data with real-world observations. However, the set of tunable parameters is usually manually selected to reduce the search space in a case-by-case manner, which is hard to scale up for complex systems and requires extensive domain knowledge. To address the scalability issue and automate the parameter-tuning process, we introduce COMPASS, which aligns the simulator with the real world by discovering the causal relationship between the environment parameters and the sim-to-real gap. Concretely, our method learns a differentiable mapping from the environment parameters to the differences between simulated and real-world robot-object trajectories. This mapping is governed by a simultaneously learned causal graph to help prune the search space of parameters, provide better interpretability, and improve generalization on unseen parameters. We perform experiments to achieve both sim-to-sim and sim-to-real transfer, and show that our method has significant improvements in trajectory alignment and task success rate over strong baselines in several challenging manipulation tasks. Demos are available on our project website: https://sites.google.com/view/sim2real-compass.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	huang23c	0	What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery	734	760	734-760	734	false	Huang, Peide and Zhang, Xilun and Cao, Ziang and Liu, Shiqi and Xu, Mengdi and Ding, Wenhao and Francis, Jonathan and Chen, Bingqing and Zhao, Ding	given family Peide Huang given family Xilun Zhang given family Ziang Cao given family Shiqi Liu given family Mengdi Xu given family Wenhao Ding given family Jonathan Francis given family Bingqing Chen given family Ding Zhao	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/huang23c/huang23c.pdf