1 citations · 2 across the 21 of their papers we have counts for
26 papers
CoSkill: Whole-Body Control via Skill Composition for Long-Horizon Human-Environment Interaction
Jeonghwan Kim, Hyeonwoo Kim, Hanbyul Joo
Achieving human-level dexterity in complex, unstructured environments requires the seamless integration of whole-body scene interaction and dexterous object manipulation skills. Wh…
Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation
Shiqi Liu, Zeyu He, Letian Tao +9
On-policy distillation (OPD) has emerged as an effective approach for large language model post-training, yet existing objectives face a trade-off between objective fidelity and op…
NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities
Haonan Jiang, Guojian Zhan, Jiancong Xie +6
Vision-language models (VLMs) increasingly power consumer-facing AI search, yet evaluating them on the diversity of everyday visual questions remains challenging. Existing benchmar…
D-MOPD: Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation
Zechen Sun, Zhiwei Zhang, Fei Zhao +7
Multi-teacher on-policy distillation (MOPD) distills several domain-expert teachers into a single student by minimizing per-domain reverse-KL divergence on the student's own rollou…
Diagnosing Compositional Generalization in Sequential Robot Tasks
Yixiao Wang, Cheng-En Wu, Lingfeng Sun +5
Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for all possible instruction tu…
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
Feihong Zhang, Guojian Zhan, Zeyu He +8
The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across comp…
Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Guojian Zhan, Letian Tao, Pengcheng Wang +6
Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling com…
STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens
Shiqi Liu, Zeyu He, Guojian Zhan +10
Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regu…
DADP: Domain Adaptive Diffusion Policy
Pengcheng Wang, Qinghang Liu, Haotian Lin +4
Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control. Substantial progress has been made t…
Bootstrap Off-policy with World Model
Guojian Zhan, Likun Wang, Xiangteng Zhang +3
Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevi…
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
Likun Wang, Xiangteng Zhang, Yinuo Wang +5
Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve o…
Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
Guojian Zhan, Likun Wang, Pengcheng Wang +4
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further pe…
Self-supervised Pretraining for Integrated Prediction and Planning of Automated Vehicles
Yangang Ren, Guojian Zhan, Chen Lv +3
Predicting the future of surrounding agents and accordingly planning a safe, goal-directed trajectory are crucial for automated vehicles. Current methods typically rely on imitatio…
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Ziang Zheng, Guojian Zhan, Shiqi Liu +3
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual ex…
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
Feihong Zhang, Guojian Zhan, Bin Shuai +3
Reinforcement learning (RL), known for its self-evolution capability, offers a promising approach to training high-level autonomous driving systems. However, handling constraints r…
Enhanced DACER Algorithm with High Diffusion Efficiency
Yinuo Wang, Likun Wang, Mining Tan +11
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended thi…
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
Ziang Zheng, Guojian Zhan, Bin Shuai +4
Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. T…
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Tianqi Zhang, Puzhen Yuan, Guojian Zhan +6
Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained…
An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability
Guojian Zhan, Qiang Ge, Haoyu Gao +3
Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging is…
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Yuxuan Jiang, Yujie Yang, Zhiqian Lan +6
Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing co…
Canonical Form of Datatic Description in Control Systems
Guojian Zhan, Ziang Zheng, Shengbo Eben Li
The design of feedback controllers is undergoing a paradigm shift from modelic (i.e., model-driven) control to datatic (i.e., data-driven) control. Canonical form of state space mo…
Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration
Zeyang Li, Chuxiong Hu, Yunan Wang +4
Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes…
Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters
Chen Chen, Zhiqian Lan, Guojian Zhan +3
There will be a long time when automated vehicles are mixed with human-driven vehicles. Understanding how drivers assess driving risks and modelling their individual differences ar…
Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning
Yangang Ren, Guojian Zhan, Liye Tang +3
Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and rob…