2 citations · 2 across the 5 of their papers we have counts for
8 papers · 1 filter
DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion
Khang Nguyen, An T. Le, Jan Peters +1
Achieving robust robot learning for humanoid locomotion is a fundamental challenge in model-based reinforcement learning (MBRL), where environmental stochasticity and randomness ca…
TD-GRPC: Temporal Difference Learning with Group Relative Policy Constraint for Humanoid Locomotion
Khang Nguyen, Khai Nguyen, An T. Le +4
Robot learning in high-dimensional control settings, such as humanoid locomotion, presents persistent challenges for reinforcement learning (RL) algorithms due to unstable dynamics…
Model Tensor Planning
An T. Le, Khai Nguyen, Minh Nhat Vu +2
Sampling-based model predictive control (MPC) offers strong performance in nonlinear and contact-rich robotic tasks, yet often suffers from poor exploration due to locally greedy s…
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
Jiayun Li, Kay Pompetzki, An Thai Le +3
Gaussian Process Motion Planning (GPMP) is a widely used framework for generating smooth trajectories within a limited compute time--an essential requirement in many robotic applic…
FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
Khang Nguyen, An T. Le, Tien Pham +3
Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capt…
Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3
Joao Carvalho, An T. Le, Philipp Jahr +4
Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large dat…