3 citations · 11 across the 32 of their papers we have counts for
14 papers · 1 filter
NeuGPT: Unified multi-modal Neural GPT
Yiqian Yang, Yiqun Duan, Hyejeong Jo +6
This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research. Traditionally, stu…
E2H: A Two-Stage Non-Invasive Neural Signal Driven Humanoid Robotic Whole-Body Control Framework
Yiqun Duan, Qiang Zhang, Jinzhao Zhou +9
Recent advancements in humanoid robotics, including the integration of hierarchical reinforcement learning-based control and the utilization of LLM planning, have significantly enh…
DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering
Jiaxu Wang, Jingkai Sun, Junhao He +4
Learning-based simulators show great potential for simulating particle dynamics when 3D groundtruth is available, but per-particle correspondences are not always accessible. The de…
Multi-Floor Zero-Shot Object Navigation Policy
Lingfeng Zhang, Hao Wang, Erjia Xiao +4
Object navigation in multi-floor environments presents a formidable challenge in robotics, requiring sophisticated spatial reasoning and adaptive exploration strategies. Traditiona…
Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models
Hao Cheng, Erjia Xiao, Yichi Wang +12
Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabul…
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Jiahang Cao, Qiang Zhang, Jingkai Sun +11
Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action traje…
Query-based Semantic Gaussian Field for Scene Representation in Reinforcement Learning
Jiaxu Wang, Ziyi Zhang, Qiang Zhang +5
Latent scene representation plays a significant role in training reinforcement learning (RL) agents. To obtain good latent vectors describing the scenes, recent works incorporate t…
Mamba as Decision Maker: Exploring Multi-scale Sequence Modeling in Offline Reinforcement Learning
Jiahang Cao, Qiang Zhang, Ziqing Wang +8
Sequential modeling has demonstrated remarkable capabilities in offline reinforcement learning (RL), with Decision Transformer (DT) being one of the most notable representatives, a…
MAD: Multi-Alignment MEG-to-Text Decoding
Yiqian Yang, Hyejeong Jo, Yiqun Duan +6
Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (…
Reinforcement Learning with Generalizable Gaussian Splatting
Jiaxu Wang, Qiang Zhang, Jingkai Sun +7
An excellent representation is crucial for reinforcement learning (RL) performance, especially in vision-based reinforcement learning tasks. The quality of the environment represen…
Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
Yiqun Duan, Qiang Zhang, Renjing Xu
The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent schol…
TriHelper: Zero-Shot Object Navigation with Dynamic Assistance
Lingfeng Zhang, Qiang Zhang, Hao Wang +4
Navigating toward specific objects in unknown environments without additional training, known as Zero-Shot object navigation, poses a significant challenge in the field of robotics…
NeuSpeech: Decode Neural signal as Speech
Yiqian Yang, Yiqun Duan, Qiang Zhang +5
Decoding language from brain dynamics is an important open direction in the realm of brain-computer interface (BCI), especially considering the rapid growth of large language model…
Whole-body Humanoid Robot Locomotion with Human Reference
Qiang Zhang, Peter Cui, David Yan +8
Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent c…