Lazy validation of Experience Graphs

Hwang, Victor; Phillips, Mike; Srinivasa, Siddhartha S.; Likhachev, Maxim

doi:10.1109/icra.2015.7139286

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…Another learning based method to reuse information from previous motion plans, the environment, and types of the obstacles is presented in [15]. Path planning using experience is also proposed in [14,32,33], where socalled experience graphs are introduced, which in fact represent a path network from previous iterations. This approach is very useful in the environment which possesses a significant amount of underlying structure that is under-utilized in a classic path planning and Information Technology and Control 2021/2/50 360 mobile manipulation.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

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This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

View full text Add to dashboard Cite

show abstract

“…Data-driven approaches have been investigated to improve the performance of traditional planners. For example, some methods store previously constructed trees or trajectories and use them for new planning tasks when the workspace is similar to before [22]- [25]. However, it is difficult to modify previously used planning structures when the environment is significantly changed.…”

Section: Related Workmentioning

confidence: 99%

Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

Huh¹,

Isler²,

Lee³

2020

Preprint

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This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-togo function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained end-to-end in a supervised fashion using costs computed from traditional motion planners. Once trained, c2g-HOF can generate a smooth and continuous cost-to-go function directly from workspace sensor inputs (represented as a point cloud in 3D or an image in 2D). At inference time, the weights of the c2g-network are computed very efficiently and near-optimal trajectories are generated by simply following the gradient of the cost-to-go function.We compare c2g-HOF with traditional planning algorithms for various robots and planning scenarios. The experimental results indicate that planning with c2g-HOF is significantly faster than other motion planning algorithms, resulting in orders of magnitude improvement when including collision checking. Furthermore, despite being trained from sparsely sampled trajectories in configuration space, c2g-HOF generalizes to generate smoother, and often lower cost, trajectories. We demonstrate cost-to-go based planning on a 7 DoF manipulator arm where motion planning in a complex workspace requires only 0.13 seconds for the entire trajectory.

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“…For example, Lien and Lu [12] proposed to construct and store local roadmaps around obstacles, then retrieve and merge those local roadmaps into a global roadmap when given a similar new environment. Similarly, Experience Graph [13]- [15] represents the connectivity of a workspace. Chamzas et al [16] used local experiences to global motion planning.…”

Section: B Motion Planning In Similar Environmentsmentioning

confidence: 99%

Designing Human-Robot Coexistence Space

Zhi

Lien

2020

Preprint

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When the human-robot interactions become ubiquitous, the environment surrounding these interactions will have significant impact on the safety and comfort of the human and the effectiveness and efficiency of the robot. Although most robots are designed to work in the spaces created for humans, many environments, such as living rooms and offices, can be and should be redesigned to enhance and improve human-robot collaboration and interactions. This work uses autonomous wheelchair as an example and investigates the computational design in the human-robot coexistence spaces. Given the room size and the objects O in the room, the proposed framework computes the optimal layouts of O that satisfy both human preferences and navigation constraints of the wheelchair. The key enabling technique is a motion planner that can efficiently evaluate hundreds of similar motion planning problems. Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner. Our results also show that the proposed method produces reasonable designs even for tight spaces and for users with different preferences.

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Lazy validation of Experience Graphs

Cited by 12 publications

References 15 publications

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

Designing Human-Robot Coexistence Space

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