1 citations · 1 across the 14 of their papers we have counts for
14 papers
Dream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation
Haoyu Zhang, Zecui Zeng, Bin Wang +3
Learning robotic policies requires dense rewards that remain informative when behavior departs from successful demonstrations. Progress-based rewards estimate how far an observatio…
A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems
Junzhe Xu, Zecui Zeng, Lusong Li +2
Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conv…
Not All Actions Are Equal: Rethinking Conditioning for Dexterous World Model
Zizhao Yuan, Zhengtu Liang, Taowen Wang +7
Recent advances in action-conditioned world models show promising progress in modeling complex interactions and forecasting future states under diverse action sequences. While thes…
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
Taowen Wang, Zikang Xie, Bin Yang +13
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body dyna…
What Limits Vision-and-Language Navigation ?
Yunheng Wang, Yuetong Fang, Taowen Wang +9
Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degradation when transitioning fro…
MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation
Baicheng Li, Dong Wu, Jun Li +4
Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware approaches such as SAM3D can recons…
SHARE: Sharing Human Actions across Robot Embodiments through Unified Retargeting and Tracking
Haoyu Zhang, Junzhe Xu, Shibo Jin +8
The central challenge in reusing human actions across robots is the need to realize shared intent through incompatible kinematics, dynamics, and control interfaces. We present SHAR…
Embodied Tree of Thoughts: Deliberate Manipulation Planning with Embodied World Model
Wenjiang Xu, Cindy Wang, Rui Fang +6
World models have emerged as a pivotal component in robot manipulation planning, enabling agents to predict future environmental states and reason about the consequences of actions…
ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface
Chuanyu Li, Chaoyi Liu, Daotan Wang +6
Handheld devices have opened up unprecedented opportunities to collect large-scale, high-quality demonstrations efficiently. However, existing systems often lack robust tactile sen…
Reflection-Based Task Adaptation for Self-Improving VLA
Baicheng Li, Dong Wu, Zike Yan +4
Pre-trained Vision-Language-Action (VLA) models represent a major leap towards general-purpose robots, yet efficiently adapting them to novel, specific tasks in-situ remains a sign…
Spiking Neural Networks Need High Frequency Information
Yuetong Fang, Deming Zhou, Ziqing Wang +5
Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind th…
DexSim2Real: Building Explicit World Model for Precise Articulated Object Dexterous Manipulation
Taoran Jiang, Yixuan Guan, Liqian Ma +7
Articulated objects are ubiquitous in daily life. In this paper, we present DexSim2Real, a novel framework for goal-conditioned articulated object manipulation. The core of o…
Empowering Embodied Manipulation: A Bimanual-Mobile Robot Manipulation Dataset for Household Tasks
Tianle Zhang, Dongjiang Li, Yihang Li +8
The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these envi…
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning
Tianle Zhang, Jiayi Guan, Lin Zhao +8
Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets. Recently, due to their powerful representational capabilities, diffusion mode…