2 citations · 8 across the 27 of their papers we have counts for
20 papers · 1 filter
Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity
Thanh Ly, Chien Le, Truong-Duy Dang +4
State-of-the-art perceptive humanoid locomotion is mostly built on retargeted motion capture, a generative motion prior, and a distillation stage. We report a complete system that…
CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger +6
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or…
CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets
Tan-Dzung Do, Cuc T. Trinh, Tuan Dat Phuong +4
Whole-body compliant control is essential for deploying heavy humanoids under high payload in human-centric environments. Most prior force-aware learning-based pipelines focus on e…
FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation
Khang H. Nguyen, Hoang Pham Quang Nguyen, Ha Phuong Nguyen +6
Adapting vision-language navigation (VLN) policies to new environments is expensive because every additional route and instruction requires an embodied demonstration. Yet standard…
PolyStepOR: Learning to Decide Without Optimal Decisions
Viet The Nguyen, Gunther Gust, An Thai Le
Decision-focused learning (DFL) trains predictors for downstream decision quality, but often relies on optimal reference decisions that are expensive to obtain. We present PolyStep…
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen +5
Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework…
vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation
Khanh D. Nguyen, Hoang M. Truong, An T. Le
Deploying language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between policy queries. We present vla.simd,…
Anytime Global Tensor Motion Planning
Sai Coumar, An T. Le, Zachary Kingston
Global Tensor Motion Planning (GTMP) solves motion planning with batched tensor operations over a layered multipartite graph. We generalize GTMP so that adjacent-layer edges are re…
RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
Minh-Loi Nguyen, Nghiem T. Diep, Nghiem Tuong Diep +19
Robot world models now generate synthetic manipulation videos, but evaluating them is hard: realistic-looking outputs often violate physical laws, temporal consistency or task logi…
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose pro…
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20
Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-shot imitation learning remains…
EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models
Thien-Loc Ha, Quang-Tan Nguyen, Trong-Bao Ho +8
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for generalist robot manipulation, yet they lack geometric inductive biases: policies trained at specific or…
Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection
Trong-Bao Ho, Quang-Tan Nguyen, Thien-Loc Ha +7
Action chunking enables robot policies to produce temporally coherent behavior, but generating multi-step action sequences with flow-based policies incurs latency that is incompati…
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Magnus Dierking, Joao Carvalho, An Thai Le +2
Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with massively parallel GPU simul…
Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing
Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu +15
Diffusion-based Vision-Language-Action (VLA) policies enable strong generalization in robotic manipulation, but remain sensitive to spurious visual correlations and noisy action ge…
TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion
Thanh Ly, Truong-Duy Dang, Chien Le +5
Foothold selection on structured terrain requires explicit reasoning about contact planarity, surface steepness, and kinematic reachability, properties not captured by a single hei…
vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models
Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen +5
Deploying vision--language--action (VLA) models on robots requires adapting model-specific inference pipelines to heterogeneous processors and limited onboard memory. We present vl…
Training Non-Differentiable Networks via Optimal Transport
An T. Le
Hard thresholds, quantization, and discrete routing can produce training losses with flat regions and jumps, where ordinary gradients vanish or are undefined. We introduce PolyStep…
AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT
An T. Le, Vien Ngo
We introduce \textbf{AAC} (Architecturally Admissible Compressor), a differentiable landmark-selection module for ALT (A*, Landmarks, and Triangle inequality) shortest-path heurist…
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen +17
Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-attention, often without retraining.…