14 citations · 53 across the 19 of their papers we have counts for
19 papers
A Hybrid Optimization Framework for Grasp Synthesis under Partial Observations
Wenzheng Zhang, Fahira Afzal Maken, Tin Lai +1
We propose a hybrid grasp synthesis framework that combines a learning-based Energy-Based Model (EBM) with an analytical Iterative Closest Point (ICP) method to generate robust gra…
Path Signatures for Diversity in Probabilistic Trajectory Optimisation
Lucas Barcelos, Tin Lai, Rafael Oliveira +2
Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated. In complex environments with several obs…
Real-time Aerial Detection and Reasoning on Embedded-UAVs
Tin Lai
We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision…
A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-based Semantic Scene Understanding
Tin Lai
Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while…
Discover Life Skills for Planning with Bandits via Observing and Learning How the World Works
Tin Lai
We propose a novel approach for planning agents to compose abstract skills via observing and learning from historical interactions with the world. Our framework operates in a Marko…
L4KDE: Learning for KinoDynamic Tree Expansion
Tin Lai, Weiming Zhi, Tucker Hermans +1
We present the Learning for KinoDynamic Tree Expansion (L4KDE) method for kinodynamic planning. Tree-based planning approaches, such as rapidly exploring random tree (RRT), are the…
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…
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Weiming Zhi, Tin Lai, Lionel Ott +1
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This pape…
Robust Hierarchical Planning with Policy Delegation
Tin Lai, Philippe Morere
We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skill…
Learning to Plan Optimally with Flow-based Motion Planner
Tin Lai, Fabio Ramos
Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The ma…
OCTNet: Trajectory Generation in New Environments from Past Experiences
Weiming Zhi, Tin Lai, Lionel Ott +2
Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and unders…
Bayesian Local Sampling-based Planning
Tin Lai, Philippe Morere, Fabio Ramos +1
Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic com…
Occ-Traj120: Occupancy Maps with Associated Trajectories
Tin Lai, Weiming Zhi, Fabio Ramos
Trajectory modelling had been the principal research area for understanding and anticipating human behaviour. Predicting the dynamic path by observing the agent and its surrounding…
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Tin Lai, Fabio Ramos, Gilad Francis
Sampling efficiency in a highly constrained environment has long been a major challenge for sampling-based planners. In this work, we propose Rapidly-exploring Random disjointed-Tr…