1 citations · 2 across the 8 of their papers we have counts for
10 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…
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…
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…
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…
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…
DexDiff: Towards Extrinsic Dexterity Manipulation of Ungraspable Objects in Unrestricted Environments
Chengzhong Ma, Houxue Yang, Hanbo Zhang +5
Grasping large and flat objects (e.g. a book or a pan) is often regarded as an ungraspable task, which poses significant challenges due to the unreachable grasping poses. Previous…
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…