1 citations · 1 across the 14 of their papers we have counts for
16 papers
Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies
Xinzhe Chen, Liqi Huang, Sihua Ren +5
SE(3)-equivariance provides a useful geometric inductive bias for visuomotor policies, encoding that actions covary with rigid transformations of the observation. However, existing…
OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation
Xinzhe Chen, Sihua Ren, Liqi Huang +5
Recent vision-language-action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with auxiliary spatial features or…
AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation
Mingyang Li, Haofan Xu, Haowen Sun +10
Many everyday robot manipulation skills are affordance-dependent, with success determined by whether the robot contacts the functional object region required by the subsequent acti…
ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
Yanbin Hu, Jin Cui, Jiayi Lu +6
Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing memory-augmented architectures…
VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection
Haowen Sun, Shaolong Zhang, Mingyang Li +6
Open-vocabulary 3D affordance detection requires localizing interaction regions on point clouds given novel affordance descriptions. Recent methods extend multimodal large language…
GeoBlock: Inferring Block Granularity from Dependency Geometry in Diffusion Language Models
Lipeng Wan, Junjie Ma, Jianhui Gu +3
Block diffusion enables efficient parallel refinement in diffusion language models, but its decoding behavior depends critically on block size. Existing block-sizing strategies rel…
Progressive Refinement Regulation for Accelerating Diffusion Language Model Decoding
Lipeng Wan, Jianhui Gu, Junjie Ma +4
Diffusion language models generate text through iterative denoising under a uniform refinement rule applied to all tokens. However, tokens stabilize at different rates in practice,…
Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion
Tianyang Wu, Hanwei Guo, Yuhang Wang +6
Reinforcement learning has shown strong promise for quadrupedal agile locomotion, even with proprioception-only sensing. In practice, however, sim-to-real gap and reward overfittin…
Bridging Simulation and Reality: Cross-Domain Transfer with Semantic 2D Gaussian Splatting
Jian Tang, Pu Pang, Haowen Sun +4
Cross-domain transfer in robotic manipulation remains a longstanding challenge due to the significant domain gap between simulated and real-world environments. Existing methods suc…
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations
Haowen Sun, Han Wang, Chengzhong Ma +4
Learning from few demonstrations to develop policies robust to variations in robot initial positions and object poses is a problem of significant practical interest in robotics. Co…
Playing Non-Embedded Card-Based Games with Reinforcement Learning
Tianyang Wu, Lipeng Wan, Yuhang Wang +2
Significant progress has been made in AI for games, including board games, MOBA, and RTS games. However, complex agents are typically developed in an embedded manner, directly acce…
Bootstrapped Model Predictive Control
Yuhang Wang, Hanwei Guo, Sizhe Wang +2
Model Predictive Control (MPC) has been demonstrated to be effective in continuous control tasks. When a world model and a value function are available, planning a sequence of acti…
MInCo: Mitigating Information Conflicts in Distracted Visual Model-based Reinforcement Learning
Shiguang Sun, Hanbo Zhang, Zeyang Liu +4
Existing visual model-based reinforcement learning (MBRL) algorithms with observation reconstruction often suffer from information conflicts, making it difficult to learn compact r…
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation
Zhihong Liu, Long Qian, Zeyang Liu +3
Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the…
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
Zeyang Liu, Xinrui Yang, Shiguang Sun +4
Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce…
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
Zeyang Liu, Lipeng Wan, Xinrui Yang +3
Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intr…