5 citations · 10 across the 12 of their papers we have counts for
16 papers
Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies
Xuehui Yu, Eason Yu, Meiyi Wang +3
Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the action, and the policy needs a memo…
Agent Priors-guided Policy Learning
Puming Jiang, Tianrun Hu, Haozhe Du +4
Robots that learn from a few demonstrations often require two forms of generalization. Compositional generalization recombines skills to solve new tasks, and skill generalization l…
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
Heterogeneous Tactile Transformer
Jianxin Bi, Qiang Wang, Jayaram Reddy +4
Tactile sensors are inherently heterogeneous: a model trained on one sensor cannot be directly used on another, which limits learning contact-rich manipulation policies from divers…
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
Xuehui Yu, Fucheng Cai, Meiyi Wang +2
Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provi…
Guided Streaming Stochastic Interpolant Policy
Puming Jiang, Meiyi Wang, Kelvin Lin +2
Inference-time guidance is essential for steering generative robot policies toward dynamic objectives without retraining, yet existing methods are largely confined to chunk-based a…
Grounded World Model: Latent Planning with Language Goals
Quanyi Li, Lan Feng, Haonan Zhang +4
World models such as DINO-WM and LeWM specify the goal with an image, which is difficult to obtain in advance for novel tasks. We present the Grounded World Model (GWM), a latent w…
SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse
Xuanran Zhai, Zekai Huang, Longyan Wu +5
Recent progress in vision-language-action (VLA) models has demonstrated strong potential for dual-arm manipulation, enabling complex behaviors and generalization to unseen environm…
Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
Ce Hao, Xuanran Zhai, Yaohua Liu +1
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size an…
VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback
Jianxin Bi, Kevin Yuchen Ma, Ce Hao +2
Tactile feedback is generally recognized to be crucial for effective interaction with the physical world. However, state-of-the-art Vision-Language-Action (VLA) models lack the abi…
Demonstrating the Octopi-1.5 Visual-Tactile-Language Model
Samson Yu, Kelvin Lin, Harold Soh
Touch is recognized as a vital sense for humans and an equally important modality for robots, especially for dexterous manipulation, material identification, and scenarios involvin…
CHD: Coupled Hierarchical Diffusion for Long-Horizon Tasks
Ce Hao, Anxing Xiao, Zhiwei Xue +1
Diffusion-based planners have shown strong performance in short-horizon tasks but often fail in complex, long-horizon settings. We trace the failure to loose coupling between high-…
Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
Jianxin Bi, Kelvin Lim, Kaiqi Chen +2
Recent advances in diffusion-based robot policies have demonstrated significant potential in imitating multi-modal behaviors. However, these approaches typically require large quan…
Octopi: Object Property Reasoning with Large Tactile-Language Models
Samson Yu, Kelvin Lin, Anxing Xiao +2
Physical reasoning is important for effective robot manipulation. Recent work has investigated both vision and language modalities for physical reasoning; vision can reveal informa…
DISCO: Language-Guided Manipulation with Diffusion Policies and Constrained Inpainting
Ce Hao, Kelvin Lin, Zhiwei Xue +2
Diffusion policies have demonstrated strong performance in generative modeling, making them promising for robotic manipulation guided by natural language instructions. However, gen…
Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion
Kaiqi Chen, Eugene Lim, Kelvin Lin +2
Imitation learning empowers artificial agents to mimic behavior by learning from demonstrations. Recently, diffusion models, which have the ability to model high-dimensional and mu…