10 citations · 40 across the 23 of their papers we have counts for
25 papers
World Value Models for Robotic Manipulation
Zhihao Wang, Jianxiong Li, Yu Cui +4
Generalist value models play a pivotal role in scaling robotic policy learning from large-scale, mixed-quality data. Mathematically, accurate value estimation demands deep temporal…
TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models
Wenbo Zhang, Jianxiong Li, Shuai Yang +4
Vision-Language-Action (VLA) models trained on large-scale data have made remarkable progress, but they remain vulnerable to distribution shifts at deployment time. Recent VLA mode…
Demystifying Action Space Design for Robotic Manipulation Policies
Yuchun Feng, Jinliang Zheng, Zhihao Wang +5
The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the optimization landscape of policy learn…
Dichotomous Diffusion Policy Optimization
Ruiming Liang, Yinan Zheng, Kexin Zheng +9
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inf…
Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
Tianyi Tan, Yinan Zheng, Ruiming Liang +6
Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. Learning-based approaches attempt to address this chall…
X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
Jinliang Zheng, Jianxiong Li, Zhihao Wang +12
Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To…
PhysiAgent: An Embodied Agent Framework in Physical World
Zhihao Wang, Jianxiong Li, Jinliang Zheng +6
Vision-Language-Action (VLA) models have achieved notable success but often struggle with limited generalizations. To address this, integrating generalized Vision-Language Models (…
Efficient Robotic Policy Learning via Latent Space Backward Planning
Dongxiu Liu, Haoyi Niu, Zhihao Wang +6
Current robotic planning methods often rely on predicting multi-frame images with full pixel details. While this fine-grained approach can serve as a generic world model, it introd…
Skill Expansion and Composition in Parameter Space
Tenglong Liu, Jianxiong Li, Yinan Zheng +4
Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of aut…
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Yinan Zheng, Ruiming Liang, Kexin Zheng +8
Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imi…
Universal Actions for Enhanced Embodied Foundation Models
Jinliang Zheng, Jianxiong Li, Dongxiu Liu +7
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticea…
Are Expressive Models Truly Necessary for Offline RL?
Guan Wang, Haoyi Niu, Jianxiong Li +3
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL…
Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning
Jianxiong Li, Zhihao Wang, Jinliang Zheng +8
Multimodal task specification is essential for enhanced robotic performance, where \textit{Cross-modality Alignment} enables the robot to holistically understand complex task instr…
xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing
Haoyi Niu, Qimao Chen, Tenglong Liu +5
Reusing pre-collected data from different domains is an appealing solution for decision-making tasks, especially when data in the target domain are limited. Existing cross-domain p…
Instruction-Guided Visual Masking
Jinliang Zheng, Jianxiong Li, Sijie Cheng +6
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targ…
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Jianxiong Li, Jinliang Zheng, Yinan Zheng +9
Multimodal pretraining is an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progressions; 2)…
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Yinan Zheng, Jianxiong Li, Dongjie Yu +4
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning. Most existing methods only enforce soft constraints, i.e., constraining safety…
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
Jianxiong Li, Shichao Lin, Tianyu Shi +5
The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement learning (RL) techniques have emerged as a popul…
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Jianxiong Li, Xiao Hu, Haoran Xu +3
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. H…
Query-Policy Misalignment in Preference-Based Reinforcement Learning
Xiao Hu, Jianxiong Li, Xianyuan Zhan +2
Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents' behavior with human desired outcomes, but is often restrained by costly human feedback. To…
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Haoran Xu, Li Jiang, Jianxiong Li +4
Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the devi…
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
Jianxiong Li, Xiao Hu, Haoran Xu +4
Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to des…
A Policy-Guided Imitation Approach for Offline Reinforcement Learning
Haoran Xu, Li Jiang, Jianxiong Li +1
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution…
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
Jianxiong Li, Xianyuan Zhan, Haoran Xu +3
In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, exi…