1 citations · 1 across the 10 of their papers we have counts for
13 papers
UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning
Yudong Lin, Haoyuan Deng, Zhuoxuan Yuan +3
Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing int…
DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents
Haoyuan Deng, Jiebin Liu, Tengxiao Zhang +3
Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires coordination between semantic reasoning and physical execution. Semantic reason…
Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
Haoyuan Deng, Haichao Liu, Wenkai Guo +6
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundat…
UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
Haoyuan Deng, Yitong Gao, Yudong Lin +3
Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.…
Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
Hongye Cao, Nuo Yan, Haoyuan Deng +5
Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating exter…
UniManip: Open-World Robotic Manipulation via Agentic Operational Graphs
Haichao Liu, Yuanjiang Xue, Yuheng Zhou +5
Achieving general-purpose robotic manipulation requires robots to bridge high-level semantic intent with low-level physical interaction in unstructured environments. However, zero-…
E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning
Haoyuan Deng, Yudong Lin, Yuanjiang Xue +5
Human-in-the-loop guidance has emerged as an effective approach for accelerating online reinforcement learning (RL) in real-world manipulation. However, existing human-in-the-loop…
NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
Chia-Yu Hung, Navonil Majumder, Haoyuan Deng +7
Vision--language--action (VLA) models have recently shown promising performance on a variety of embodied tasks, yet they still fall short in reliability and generalization, especia…
MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation
Runhao Li, Wenkai Guo, Zhenyu Wu +5
Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these mode…
VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search
Wenkai Guo, Guanxing Lu, Haoyuan Deng +3
Vision-Language-Action models (VLAs) achieve strong performance in general robotic manipulation tasks by scaling imitation learning. However, existing VLAs are limited to predictin…
SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
Haoyuan Deng, Wenkai Guo, Qianzhun Wang +2
Bimanual manipulation has been widely applied in household services and manufacturing, which enables the complex task completion with coordination requirements. Recent diffusion-ba…
ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model
Tengbo Yu, Guanxing Lu, Zaijia Yang +7
Multi-task robotic bimanual manipulation is becoming increasingly popular as it enables sophisticated tasks that require diverse dual-arm collaboration patterns. Compared to uniman…
AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation
Guanxing Lu, Tengbo Yu, Haoyuan Deng +3
Performing general language-conditioned bimanual manipulation tasks is of great importance for many applications ranging from household service to industrial assembly. However, col…