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A Unifying Variatio...
A Unifying Variational Framework for Gaussian Process Motion Planning
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- Cosier, Lucas (author)
- University College London (UCL)
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- Ioardan, Rares (author)
- University College London (UCL)
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- Zwane, Sicelukwanda (author)
- University College London (UCL)
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- Franzese, Giovanni (author)
- Technische Universiteit Delft,Delft University of Technology (TU Delft)
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- Wilson, James T (author)
- Imperial College of Science, Technology and Medicine
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- Deisenroth, Marc Peter (author)
- University College London (UCL)
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- Terenin, Alexander (author)
- Cornell University
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- Bekiroglu, Yasemin, 1982 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Proceedings of Machine Learning Research. - 2640-3498. ; 238
- Related links:
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Abstract
Subject headings
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- To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on variational Gaussian Processes, which unifies and generalizes various probabilistic- inference-based motion planning algorithms. Our framework provides a principled and flexible way to incorporate equality-based, inequality-based, and soft motion- planning constraints during end-to-end training, is straightforward to implement, and provides both interval-based and Monte-Carlo-based uncertainty estimates. We conduct experiments using different environments and robots, comparing against baseline approaches based on the feasibility of the planned paths, and obstacle avoidance quality. Results show that our proposed approach yields a good balance between success rates and path quality.
Subject headings
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Keyword
- Gaussian Processes
- Robotic manipulation
- Motion Planning
Publication and Content Type
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