Learning Implicit Sampling Distributions for Motion Planning

Zhang, Clark; Huh, Jinwook; Lee, Daniel D.

doi:10.48550/arxiv.1806.01968

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…There has been a lot of recent effort on finding low dimensional structure in planning [31]. In particular, generative modeling tools like variational autoencoders [32] have been used to great success [33][34][35][36][37]. We base our work on Ichter et al [11] where a CVAE is trained to learn the shortest path distribution.…”

Section: Related Workmentioning

confidence: 99%

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

Kumar

Mandalika

Choudhury

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a lowcost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing.We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficultto-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.

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Section: Related Workmentioning

confidence: 99%

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

Kumar

Mandalika

Choudhury

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…In Ha et al (2018), Gaussian process dynamical models (Wang et al 2008) served as a latent dynamical model and was utilized for planning in a similar way with this work. Though the dynamics were not considered, Ichter et al (2018), Zhang et al (2018) used the conditional VAEs to learn a non-uniform sampling methodology of a sampling-based motion planning algorithm.…”

Section: Related Workmentioning

confidence: 99%

Adaptive path-integral autoencoder: representation learning and planning for dynamical systems

Ha¹,

Park²,

Chae³

et al. 2019

J. Stat. Mech.

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We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional sequential raw data, e.g., video. The framework builds upon recent advances in amortized inference methods that use both an inference network and a refinement procedure to output samples from a variational distribution given an observation sequence, and takes advantage of the duality between control and inference to approximately solve the intractable inference problem using the path integral control approach. The learned dynamical model can be used to predict and plan the future states; we also present the efficient planning method that exploits the learned low-dimensional latent dynamics. Numerical experiments show that the proposed path-integral control based variational inference method leads to tighter lower bounds in statistical model learning of sequential data. The supplementary video 1 and the implementation code 2 are available online.

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Learning Implicit Sampling Distributions for Motion Planning

Cited by 2 publications

References 14 publications

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

Adaptive path-integral autoencoder: representation learning and planning for dynamical systems

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