14 citations · 53 across the 19 of their papers we have counts for
6 papers · 1 filter
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Xipei Wang, Haoyu Zhang, Yuanbo Zhang +4
The era of information explosion had prompted the accumulation of a tremendous amount of time-series data, including stationary and non-stationary time-series data. State-of-the-ar…
sbp-env: Sampling-based Motion Planners' Testing Environment
Tin Lai
Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses o…
Parallelised Diffeomorphic Sampling-based Motion Planning
Tin Lai, Weiming Zhi, Tucker Hermans +1
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphi…
Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph
Tin Lai, Fabio Ramos
In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying configuration space, affecting all subsequ…
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Weiming Zhi, Tin Lai, Lionel Ott +2
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs). In the context of machine lear…
Rapidly-exploring Random Forest: Adaptively Exploits Local Structure with Generalised Multi-Trees Motion Planning
Tin Lai
Sampling-based motion planners perform exceptionally well in robotic applications that operate in high-dimensional space. However, most works often constrain the planning workspace…